A Systematic Review of Building Energy Consumption Prediction: From Perspectives of Load Classification, Data-Driven Frameworks, and Future Directions

Abstract

The rapid development of machine learning and artificial intelligence technologies has promoted the widespread application of data-driven algorithms in the field of building energy consumption prediction. This study comprehensively explores diversified prediction strategies for different time scales, building types, and energy consumption forms, constructing a framework for artificial intelligence technologies in this field. With the prediction process as the core, it deeply analyzes the four key aspects of data acquisition, feature selection, model construction, and evaluation. The review covers three data acquisition methods, considers seven key factors affecting building loads, and introduces four efficient feature extraction techniques. Meanwhile, it conducts an in-depth analysis of mainstream prediction models, clarifying their unique advantages and applicable scenarios when dealing with complex energy consumption data. By systematically combing the existing research, this paper evaluates the advantages, disadvantages, and applicability of each method and provides insights into future development trends, offering clear research directions and guidance for researchers.

1. Introduction

The energy demand for buildings is increasing globally and gradually becoming a major contributor to global energy consumption. The report from the International Energy Agency shows that the energy consumption of the construction industry accounts for over 33% of the global total energy consumption [1,2]. Due to the continuous growth of the population and the acceleration of urbanization, the energy required for buildings is constantly increasing. In the United States and the European Union, the energy demand of the construction industry accounts for over 40% of all energy consumption [3,4]. In China, building energy consumption contributes 20% of total energy consumption, and the carbon dioxide emissions of the construction industry in these three regions all exceed 30% [5]. Therefore, reducing building energy consumption is the primary issue that countries around the world need to face in energy conservation and emission reduction.

Building energy consumption prediction is one of the most effective means of improving energy efficiency in existing technologies. Accurate energy consumption prediction is the foundation of energy network scheduling, which helps to achieve matching and regulation of energy supply and demand, ensures the low-cost and efficient operation of building energy systems, and provides auxiliary decision-making and data support for building managers to formulate optimal operational strategies [6,7]. Physical modeling methods and data-driven methods are the two main methods for predicting building energy consumption [8,9].

The physical modeling method models the characteristics of the building itself, which can represent the building characteristics of different parts, materials, and uses, and considers the interaction of building elements. Through physical models, building energy consumption prediction can conduct multi-dimensional and multi-dimensional performance analysis and optimization of buildings during the design and operation stages, including building structure, exterior walls, lighting, fire ventilation, heating and ventilation, wind and solar energy utilization, and other aspects. Building energy consumption simulation software such as Energy-plus and e-Quest have become very mature in physical modeling and can simulate multiple types of building energy consumption systems [10]. However, the real-time interaction between residents, energy systems, and the environment is very complex, and it is difficult for physical methods to reflect the real interactions, resulting in unsatisfactory accuracy of physical modeling methods [11]. Although some researchers have proposed optimization methods, such as Bayesian estimation, to improve accuracy [12], these methods may increase costs and make it difficult to achieve online applications. Therefore, physical modeling methods may be more suitable for estimation during the architectural design phase rather than accurate online prediction [13].

The data-driven approach utilizes historical operational data of buildings to analyze the main factors affecting energy consumption and constructs data models using machine learning or deep learning techniques to simulate the real process of building energy consumption. This type of method usually consists of four steps: data collection, data preprocessing, model training and testing, and model evaluation and correction [8]. In the data collection phase, real operational data of the building is obtained for the establishment of the data model [14]; in the data preprocessing phase, the data are cleaned, the correlation between features and energy consumption is analyzed, the main features are screened, and the feature dimensions are unified [15]; during the model training and testing phase, regression, neural network and other techniques are used to construct the data model, and the optimal hyper-parameters of the model are obtained through testing [16]; during the model evaluation and revision phase, universal indicators such as mean absolute percentage error (MAPE), root mean square error (RMSE), etc. are used to evaluate the model results [17]. Then, apply the model to real online operations, and the model parameters are continuously optimized based on the feedback results. The data-driven approach is more reflective of the operational status of building energy systems than physical modeling methods. The data model can be trained online and continuously revised and optimized, suitable for predicting energy consumption trends and future energy demand, which is beneficial for building managers to allocate energy usage reasonably, optimize energy structure, and make wise decisions.

In the current context of rapid technological development, a series of emerging technologies are having a transformative impact on the field of building energy consumption prediction. The rise of the Internet of Things (IoT) technology enables the interconnection of various devices within buildings, allowing for the real-time collection of massive amounts of energy data. This provides a richer and more accurate data source for data-driven methods, greatly enhancing the reliability and accuracy of energy consumption prediction models.

With its powerful data storage, processing, and analysis capabilities, big data technology can conduct in-depth mining of vast amounts of building energy consumption data, uncovering potential energy consumption patterns and laws, thus assisting in optimizing prediction models. In addition, the continuous innovation in the field of artificial intelligence, especially the continuous evolution of deep-learning algorithms, makes it possible to build more complex and intelligent energy consumption prediction models. For example, models based on recurrent neural networks (RNNs) and their variant long short-term memory networks (LSTMs) can effectively process time-series data and capture the dynamic characteristics of building energy consumption changes over time, demonstrating excellent performance in both short-term and long-term energy consumption predictions. Generative adversarial networks (GANs) have also begun to be applied in the field of building energy consumption prediction. By generating simulated data to expand the training dataset, the generalization ability of the model is improved. The integrated application of these emerging technologies is expected to reshape the technological landscape of building energy consumption prediction and provide strong support for more efficient and accurate energy consumption prediction.

In the current context of global sustainable development, research in the field of building energy consumption prediction is of vital significance for alleviating the energy crisis and reducing carbon emissions. Although previous studies have achieved certain results, with the rapid development of technology and the increasingly severe energy situation, there are still many gaps that urgently need to be filled.

The core motivation for writing this review paper is as follows. On the one hand, the continuous innovation of building energy consumption prediction technologies makes it necessary for us to sort out the latest research progress to meet the changing energy demands and the needs of building operation and management. On the other hand, existing reviews mostly focus on traditional machine learning algorithms, lacking a comprehensive and in-depth summary of deep learning technologies that have emerged in the field of building energy consumption prediction in recent years. Especially in the past six years, deep learning has demonstrated great potential in aspects such as data processing and improvement of model accuracy, and its technical route has undergone tremendous changes. However, there is a severe shortage of relevant, comprehensive reviews.

The work of this paper systematically integrates the latest progress of deep-learning-based building energy consumption prediction methods, breaking through the limitations of only focusing on traditional machine learning algorithms in the past. Through an in-depth analysis of the multi-dimensional building load classification system, it is possible to more accurately grasp the characteristics of building energy consumption in different scenarios, providing a solid foundation for the subsequent construction of data-driven models. In addition, taking the prediction steps as the main line, a comprehensive and scientific data-driven energy consumption prediction framework is innovatively established, elaborating on the cutting-edge technologies and methods in each link from data collection to model validation. This provides a new perspective and operation guide for future research, helping to improve the accuracy and reliability of building energy consumption prediction and contributing to the building industry’s achievement of more efficient energy management and energy conservation and emission reduction goals. The main structure of this paper is shown in Figure 1. The paper is organized as follows.

Section 2 provides a brief overview of the review articles on building energy consumption prediction in recent years. Section 3 delves into the multifaceted classification system of building load types. This system meticulously categorizes loads based on three key dimensions: time span, building type, and energy type, aiming to comprehensively cover and precisely identify load characteristics across diverse scenarios. Section 4 establishes a systematic, data-driven framework for energy consumption forecasting. It provides a thorough exposition of each critical stage within this framework, highlighting the prevalent techniques and methodologies employed in data collection, preprocessing, model development, training, and optimization, as well as result validation. This ensures the scientific rigor and accuracy of the forecasting process. Section 5 offers a forward-looking perspective on the future research directions in energy consumption forecasting and related fields. It identifies potential emerging trends, challenges, and corresponding strategies, thereby illuminating the path for future research endeavors and practical applications. Lastly, Section 6, serving as the conclusion, reviews the primary arguments and findings presented throughout the document. It underscores the significance of nuanced classification of building load types and innovative approaches to energy consumption forecasting. Furthermore, it summarizes the practical implications of the research findings in advancing energy efficiency and fostering the development of green buildings.

2. Recent Review Articles

To comprehensively and systematically obtain relevant literature on building energy consumption prediction, we have comprehensively utilized multiple authoritative academic platforms for literature retrieval. We conducted searches on major academic platforms such as Web of Science, Google Scholar, Scopus, SpringerLink, IEEE Xplore, and Wiley Online Library using the keyword “overview of building energy consumption prediction”. We also carried out extensive searches by combining keywords closely related to building energy consumption prediction, such as “building energy consumption prediction” and “data-driven approach in building energy”.

During the literature screening process, strict inclusion and exclusion criteria were set. The inclusion criteria were review articles that focused on the field of building energy consumption prediction and were published between 2000 and 2025. The exclusion criteria included literature that was irrelevant to the research topic, duplicate publications, and literature with severely substandard quality. After multiple rounds of screening, a total of 21 high-quality review articles, as shown in

Table 1. Statistics of Review Articles on Building Energy Consumption Prediction from 2018 to 2025.

Ref.	Year	Building Type					Load Type			Prediction Model
		Ind	Edu	Off	Com	Res	PL	TL	WL	Clust	Reg	MLP	DL
Amasyali et al. [8]	2018	✓	✓	✓	✓	✓	✓	✓	✓		✓	✓
Ahmad et al. [18]	2018						✓	✓	✓	✓	✓	✓
Wei et al. [19]	2018	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Tian et al. [20]	2018						✓	✓			✓
Fan et al. [21]	2019							✓					✓
Bourdeau et al. [22]	2019		✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Ahmad et al. [23]	2020						✓				✓	✓	✓
Fu et al. [24]	2022						✓	✓	✓				✓
Lu et al. [25]	2022	✓	✓	✓	✓	✓	✓	✓	✓		✓	✓	✓
Chen et al. [26]	2022						✓				✓	✓	✓
Ardabili et al. [27]	2022						✓				✓	✓
Jin et al. [28]	2023						✓	✓			✓	✓
Gao et al. [29]	2023	✓	✓	✓	✓	✓	✓	✓	✓		✓	✓
Liu et al. [30]	2023	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Afzal et al. [31]	2023						✓	✓	✓			✓	✓
Olu-Ajayi et al. [32]	2023				✓	✓	✓	✓	✓		✓	✓
Chen et al. [33]	2023						✓			✓	✓	✓	✓
Balali et al. [34]	2023							✓				✓
Eren et al. [35]	2024						✓					✓	✓
Afzal et al. [36]	2024					✓		✓	✓				✓
Lu et al. [37]	2025	✓	✓	✓	✓	✓	✓	✓		✓	✓		✓

Abbreviation: Ind: industrial buildings, Edu: educational buildings, Off: office buildings, Com: commercial buildings, Res: residential buildings, PL: power load, TL: thermal load, WL: water load, Clust: clustering-based, Reg: regression, MLP: multi-layer perceptron, DL: deep learning.

, and 170 high-quality research articles were finally collected from various platforms. These articles form the basis of the literature review of this study.

Through a systematic review of 21 review literatures, the existing research presents three significant characteristics: Firstly, in terms of the evolution of methodology, machine learning technologies have always dominated. Among them, SVR has become a frequently mentioned benchmark model due to its robustness and interpretability. In recent years, deep learning methods have shown a rapid growth trend, especially convolutional neural networks (CNN) and LSTM, which have demonstrated unique advantages in handling temporal features. Secondly, regarding the distribution of research focuses, the issue of data quality has become a consensus challenge across different fields. Approximately 65% of the literature involves preprocessing techniques such as data cleaning, missing value imputation, and standardization. However, there are still significant differences in feature engineering strategies for building heterogeneity (such as the differences in energy consumption between office buildings and residential buildings). Thirdly, in terms of the expansion of application scenarios, the prediction objectives show a diversified trend. In addition to traditional power load prediction, the proportion of research on predicting thermal load, cooling load, and water load has been increasing year by year. Especially, combined load prediction has gradually become an emerging research direction.

It is worth noting that there are three contradictions in the methodology of existing research: the gap between algorithm innovation and engineering implementation, the conflict between the dependence of high-precision models on the amount of data and the scarcity of data in practical scenarios, and the tension between black-box deep learning models and the demand for interpretability of the physical mechanisms of building energy consumption. This contradiction is particularly prominent in the field of industrial buildings, as the complex energy flow network and high-frequency dynamic disturbances limit the generalization ability of existing models.

In addition to focusing on the algorithms themselves, researchers have also analyzed building energy consumption prediction from multiple perspectives, including datasets, data preprocessing, feature extraction, and model application. However, there is still a lack of a comprehensive framework that can provide in-depth guidance for understanding data-driven building energy consumption prediction methods. Therefore, this paper proposes a comprehensive data-driven building energy consumption prediction framework, covering five key stages: data collection, data preprocessing, feature analysis, model prediction, and result evaluation. This framework serves as a complete guide for practical applications, identifies the specific methods used in each stage, and discusses the advantages and disadvantages of these methods.

3. Building Load Type

The prerequisite for load forecasting is to clarify the type of load, which can enable a more precise analysis of forecasting requirements [38]. Based on this foundation, load forecasting issues can be categorized in detail according to the forecasting time horizon, the types of buildings involved, and the specific load types, as illustrated in Figure 2. Such categorization helps to gain a deeper understanding of the load forecasting problem, thereby developing more accurate and targeted forecasting strategies.

3.1. Classify Based on Time Span

During the process of load forecasting, the types of load forecasting can be subdivided into four main categories based on the length of the prediction period: very short-term load forecasting, short-term load forecasting, medium-term load forecasting, and long-term load forecasting [39].

The time frame for very short-term load forecasting is limited to within one day [40], with a time resolution accurate to fine-grained units such as seconds, minutes, and hours. When the time span of the forecast extends from one day to one week, it is referred to as short-term forecasting, with its time resolution primarily measured in days and weeks. On the other hand, medium- to long-term load forecasting focuses on load fluctuations from quarters to years, providing crucial information for strategic planning. As for predictions spanning several years or more, they are categorized as long-term forecasting, which holds profound significance for strategic planning and decision-making [41].

3.1.1. Very Short Term Load Forecasting

The role of very short-term load forecasting is indispensable in power generation planning, real-time control, and power plant operations. It serves as a core strategy for achieving efficient, rapid response, and real-time scheduling in power systems of buildings and is also a critical component of modern power system management [42,43]. Through accurate, very short-term load forecasting, the safety and efficiency of power systems can be significantly improved [44,45]. However, very short-term load forecasting involves a comprehensive grasp of power generation plans, real-time control, and operational conditions within 24 h. This process is influenced by numerous complex factors, and the data characteristics exhibit non-stationary and random processes. Therefore, scientifically and reasonably analyzing load characteristics has become an extremely challenging task.

3.1.2. Short Term Load Forecasting

The short-term prediction of building energy consumption is closely linked to the daily operations of various service systems [46,47], playing a crucial role in reducing the uncertainty associated with future building operations [48]. This not only assists building managers in developing more cost-effective energy-saving strategies but also enables the optimization of building energy management and the enhancement of environmental sustainability [49]. Additionally, short-term building energy consumption prediction plays a pivotal role in driving the development of model-based predictive control, fault detection, and diagnostic methods. Currently, the prediction of short-term heating loads [50,51], cooling loads [52,53,54], and power loads [55,56,57] is a particularly hot topic of research. As illustrated in Figure 3, a stacking ensemble framework for short-term load forecasting is proposed [58]. The framework encompasses feature engineering, object determination, forecasting, and an evolution stage. It utilizes a stacking strategy to predict short-term dynamic changes in building loads.

Data-driven modeling is a commonly used approach in short-term load prediction, which adjusts and optimizes model parameters based on actual building operational data to improve prediction accuracy [59]. With the continuous advancement of deep learning technology, it has provided a more powerful tool for short-term building load prediction. Leveraging this technology, we can further enhance prediction precision, ensuring the reliability and economic efficiency of building energy management systems [60].

3.1.3. Medium Term Load Forecasting

With the continuous growth of energy demand, there is an increasingly urgent need for accurate and powerful demand predictions, which is crucial for formulating effective medium-term strategies and controlling energy usage in the construction sector [61]. However, compared with short-term load forecasting, medium-term forecasting has received relatively less attention [62]. Medium-term power load forecasting not only provides theoretical support for the reasonable maintenance of grid equipment but also plays a key role in the stable and efficient operation of the power system, generator shutdown planning, and supply and demand management [63]. The results of medium-term load forecasting can provide weekly fluctuation information, which plays an important role in planning energy maintenance, agreeing on energy matching mechanisms, and formulating energy strategies, thereby contributing to the optimization of energy system planning and scheduling [64]. Correct load forecasting can not only improve the utilization rate and reliability of electricity but also ensure that the energy system network responds quickly and fully meets user needs [65]. Bashiri Behmiri et al. [66] proposed a medium-term load forecasting method based on time-series regressions and neural networks, which takes into account the future weather sequence and improves the accuracy of the medium-term forecasting model. The basic framework of the forecasting method they proposed is shown in Figure 4. In addition, medium-term forecasting also provides valuable supporting information for system operation, maintenance planning, and long-term contract negotiations [67,68].

At the same time, the time framework of the prediction time domain varies in different studies [69,70,71]. For ultra-short-term load prediction, some studies limit it to within a few minutes to an hour, which is used for real-time monitoring. However, some other studies also include the prediction within a day, such as in the scenarios of commercial buildings. For short-term load prediction, it usually ranges from one day to one week, but some studies have shortened it to a few hours to several days, which is related to the building production process or the living patterns of residents. Medium-term load prediction generally focuses on the load fluctuations from a quarter to a year. However, in the research of specific energy markets or building projects, the time span may be shortened to several weeks or months, such as in the scenarios of energy contract negotiations or energy planning for seasonal commercial buildings. Long-term load prediction generally covers several years or more. But considering the changes in the industry and policies, some studies also regard the prediction for three to five years as a long-term prediction, for example, in the planning of the emerging green building field.

These differences stem from the characteristics of the research objects, the requirements of application scenarios, as well as the availability and quality of data. The choice of different time frameworks affects the prediction methods and model construction. For ultra-short-term and short-term load prediction, since the data changes rapidly, models that are sensitive to data changes, such as the LSTM network, are more suitable. For medium-term and long-term load prediction, as more macro factors need to be considered, statistical models such as the autoregressive integrated moving average (ARIMA) model or regression models combined with macroeconomic variables may be more applicable. In practical research and applications, it is necessary to comprehensively consider these factors to select an appropriate time framework so as to ensure the accuracy and practicality of the prediction and provide reliable support for building energy management.

3.1.4. Long Term Load Forecasting

Long-term load forecasting plays a pivotal role in the production, operation, planning, and construction of power systems. It not only needs to be closely integrated with long-term macroeconomic trends but also requires a thorough consideration of various complex factors [72,73], including uncertainties in future scenarios and trends, diverse end-uses, fuel types, and driving forces [74]. When the latest trends in time series data are difficult to obtain, accurately predicting long-term loads becomes challenging. Figure 5 presents an example of long-term load forecasting.

As the time horizon of prediction extends, the likelihood of dynamic changes becomes increasingly significant, leading to a gradual decrease in prediction accuracy [76]. As a crucial tool for ensuring the safe and economic operation of the system, long-term load forecasting has a profound impact on enterprises’ formulation of production and business objectives as well as development plans [77]. Its prediction results serve as an important basis for the planning and renovation of construction projects and can also provide guidance for the selection of construction sites, power generation methods, determination of construction scales, and scheduling of construction progress for power plant projects [78]. Therefore, long-term load forecasting holds an irreplaceable significance in grid investment decisions and energy system planning, playing a vital role in optimizing resource allocation, enhancing system efficiency, and ensuring energy security [79].

3.2. Classify Based on Building Type

Based on various building types, load forecasting can be categorized into five major groups: Industrial buildings, office buildings, commercial buildings, residential buildings, and educational buildings. Each type of building possesses its own unique load patterns and requirements. Additionally, accurate load calculations and the rational determination of power supply sources are crucial steps in ensuring the stable operation of a building’s electrical system. In practical applications, it is necessary to comprehensively consider factors such as the actual conditions of the local municipal power grid, the significance of user loads, and their specific needs to facilitate scientific and rational planning and design.

3.2.1. Industrial Buildings

Industrial buildings, designed specifically for activities such as industrial production, warehousing, and transportation, encompass a wide range of types, including factories, warehouses, logistics centers, and more. These structures must not only meet the comfort and safety needs of indoor operators but also ensure the efficiency and safety of production processes. Given the diversity of industrial sectors, there are significant differences in the indoor environmental characteristics and industrial requirements for industrial buildings across various industries [80].

The energy consumption of industrial buildings is primarily concentrated in building equipment systems, such as heating, ventilation, and air conditioning systems [81]. Notably, the energy usage of industrial buildings is closely related to weather conditions, while energy usage in production is directly linked to equipment productivity. To accurately predict these variations, it is common to combine features such as air enthalpy, solar radiation, and wind speed to predict weather dependency, while considering factors like job progress, productivity, the number of employees, and the occupied floor area to determine production dependency [82]. Neri Banti [83] reviewed 203 scientific papers and summarized the events related to industrial buildings. Figure 6 illustrates the relationship between industrial buildings and various events, where the size of the spheres indicates the frequency of occurrence and the distance between them represents the degree of correlation. It is evident from the figure that energy saving and consumption is an area of significant concern in industrial buildings.

However, due to the complexity of manufacturing processes and industrial equipment, industrial buildings often generate a significant amount of energy loads that are unrelated to weather fluctuations or building activities. Therefore, accurately predicting the energy usage of industrial buildings is both a necessary task and a complex challenge.

3.2.2. Educational Buildings

According to statistics from the U.S. Energy Information Administration, the energy consumption of educational buildings accounts for 10.79% of the total energy consumption of commercial buildings. In particular, the energy consumption of universities and colleges reaches up to 33 billion kWh, representing 24.44% of the total energy consumption of educational facilities [84]. Among various types of buildings, the energy consumption of educational buildings ranks second only to office buildings [85], but their potential for energy conservation may far exceed that of residential buildings. Therefore, accurate prediction of the energy consumption of educational buildings is crucial for reducing energy usage [86]. The schematic of the process of improving energy efficiency is shown in Figure 7.

The energy consumption characteristics of educational buildings are jointly influenced by various factors such as their scale, age, usage functions, design concepts, and local climate. Their primary energy demands stem from heating, ventilation, and air conditioning (HVAC) systems, lighting systems, and the use of various teaching and experimental equipment. Additionally, the energy consumption of different spaces within educational buildings varies according to parameters such as teaching hours, vacation schedules, and climatic changes [87].

It is worth noting that in the process of predicting building energy consumption, having more features does not necessarily mean better predictions. A deep analysis of the actual impact of each feature on building energy consumption in individual samples is crucial for campus operators and managers to accurately plan energy expenditures. By scientifically and reasonably utilizing this feature information [88], it can help achieve effective reduction in energy consumption and optimization of energy usage in educational buildings.

3.2.3. Office Buildings

Office buildings are designed for government agencies, organizations, enterprises, and institutions to handle administrative affairs and carry out various business activities. These buildings serve as the central hub for intellectual workers to conduct their daily work and activities, encompassing a range of forms including administrative office buildings, specialized office buildings [89], and more. With the advancement of the times, modern office buildings are continuously evolving towards a comprehensive and integrated direction, cleverly integrating various functional spaces such as offices, meeting rooms, commercial spaces, restaurants, and garages into a single structure, thereby forming a diversified and integrated architectural system. For the purpose of collecting data related to energy consumption in the office building, the sensor layout is as shown in Figure 8.

The energy consumption of office buildings mainly originates from service equipment such as lighting, air conditioning [90], elevators, and various office appliances. Their energy consumption characteristics are evident, primarily manifesting in relatively stable operating hours and being significantly influenced by both personnel density and climatic environmental factors.

Figure 8. Office buildings sensor placement [91].

Figure 8. Office buildings sensor placement [91].

To more effectively manage the energy consumption of office buildings, modern technology such as machine learning and deep learning is often employed for load prediction [92,93]. This prediction process is similar to that of other types of building load prediction, involving multiple crucial steps such as data processing, feature engineering, model establishment, performance evaluation, and practical application. Through these steps, we can predict the energy consumption of office buildings more accurately, thereby formulating more reasonable energy-saving measures and strategies.

3.2.4. Commercial Buildings

Commercial buildings, as the core spaces for people to conduct business activities, are notably characterized by their continuously running equipment, significant internal heat dissipation, and relatively high energy consumption per unit area. In Europe, commercial buildings account for nearly 30% of total electricity consumption, while in the United States, this proportion exceeds 35% [94]. This underscores the importance and urgency of optimizing and reducing energy use in commercial buildings.

The energy consumption of commercial buildings is influenced by a range of internal and external factors, such as weather conditions and building occupancy. These factors intermingle, resulting in complex nonlinear patterns of energy consumption [95]. Compared to office buildings, the load of commercial buildings is not only influenced by outdoor weather parameters and the building envelope but also by numerous complex factors that are difficult to monitor and predict online, such as personnel density, personnel behavior, and sales activities [96]. Therefore, the load characteristics of commercial buildings exhibit strong nonlinearity, randomness, and fluctuation. A schematic diagram of the load forecasting process for commercial buildings is shown in Figure 9.

Given these characteristics, short-term load forecasting for commercial buildings holds higher practical value and guiding significance compared to medium and long-term forecasting. To achieve accurate load forecasting, the industry has widely adopted advanced forecasting methods, including machine learning and deep learning [97,98]. However, the energy systems in commercial buildings are often intricate and complex, posing significant challenges to the assessment and prediction of load demand. Therefore, while pursuing efficient energy use, it is also crucial to continuously explore and innovate to address these challenges.

Figure 9. A schematic diagram of the load forecasting process for commercial buildings [99].

Figure 9. A schematic diagram of the load forecasting process for commercial buildings [99].

3.2.5. Residential Buildings

With the increase in population and the robust development of the economy, residential electricity consumption has risen significantly, making mid-to-short-term forecasting of the total load demand in residential communities crucial [100]. Such forecasting not only helps balance power supply and demand but also provides strong support for implementing precise demand response strategies [101]. Short-term residential load forecasting serves as a vital foundation for constructing efficient energy distribution strategies [102]. However, due to the diversity and randomness of residents’ electrical appliance usage, constructing accurate prediction models at the individual household level is indeed a challenge [103]. As shown in Figure 10, an example of load demand forecasting for residential buildings is presented.

Residential buildings’ load types mainly focus on electricity and heat [105,106]. Nowadays, with the emergence of vast residential energy consumption data, research directions in data-driven technologies have been further expanded [107]. Traditional machine learning methods struggle to model the unbalanced energy consumption patterns of residents, whereas the improvement in data foundations has made deep learning models an effective data-driven technology [108]. To enhance the accuracy of these prediction methods, it is not only necessary to properly handle noise and uncertainties in the data sources but also to optimize the parameters of the prediction models using optimization methods. This will be the key to improving prediction accuracy [109].

3.3. Classify Based on Energy Type

In the construction industry, energy load is a crucial factor in ensuring the normal operation of buildings. Among them, electrical load occupies a central position, providing energy support for lighting, power, elevators, and more. Thermal load, on the other hand, is related to winter heating, ensuring a warm and comfortable indoor environment. Water load covers both water supply and drainage, ensuring smooth provision of domestic water and wastewater treatment within the building. Together, these three components constitute the primary framework of building energy load, which is significant in improving energy efficiency and achieving green and sustainable development.

3.3.1. Power Load

Given the fluctuations in electricity prices during peak and off-peak hours, the ability to predict peak loads in advance is crucial for avoiding unnecessary additional costs [110]. Accurate predictions of energy demand, along with an understanding of anticipated peak loads, not only help ensure the reliability of power supply but also effectively reduce electricity costs for consumers. However, due to the variations in energy consumption characteristics among buildings in different regions, building accurate load prediction models remains a challenging task [111].

To improve the accuracy of load forecasting, commonly used strategies involve adopting systematic loss methods such as neural networks [112], SVR [113], and their derivative techniques [114]. The utilization of deep reinforcement learning for short-term load prediction can effectively address issues related to high temporal correlation and high convergence instability [115].

Short-term building energy consumption forecasting is a significant topic in the energy market and the operation of smart grids. Existing forecasting methods include sequence-to-sequence long short-term memory networks (S2S LSTMs), seasonal autoregressive (SAR), multi-output support vector machines (MO-SVMs), multi-layer perceptron (MLP), and clustering-based approaches [116]. These methods each have their own advantages in improving the accuracy of short-term load forecasting, providing powerful tools for energy management and optimization.

With the popularization of electric vehicles, their charging load has become an emerging and non-negligible part of the building’s power load. In various building scenarios, such as residential areas, commercial buildings, and public parking lots, the charging demand for electric vehicles is increasing day by day. Compared with traditional power loads, the charging load of electric vehicles has strong randomness and uncertainty. On the one hand, the charging time and charging amount of electric vehicles depend on factors such as the travel habits of vehicle owners, the battery capacity of the vehicles, and the remaining power. On the other hand, the simultaneous charging of a large number of electric vehicles may cause a sudden surge in power demand in a local area within a short period, putting great pressure on the power grid.

To accurately predict the charging load of electric vehicles, researchers have attempted to adopt a variety of methods. For example, a probability model based on user behavior analysis. By collecting and analyzing the charging behavior data of a large number of vehicle owners, it predicts the probability of electric vehicles accessing the power grid and the charging power at different times. And a real-time monitoring and prediction system combined with smart grid technology. It uses sensors and communication networks to obtain the charging status information of electric vehicles in real-time and then accurately estimates the future charging load.

3.3.2. Thermal Load

Thermal load refers to the thermal energy that needs to be added or removed from a space or system in order to achieve a specific temperature condition. It can encompass both the need for heating load and cooling load. Accurate thermal load prediction plays a pivotal role in the optimal planning and efficient operation of energy systems [117]. It not only effectively reduces energy waste and improves the comfort of occupants but also significantly extends the service life of building equipment [118]. Accurate prediction of thermal load serves as a crucial foundation for efficient energy planning. Given the significant and complex time delay effects between building heat load and various influencing factors, it is imperative to ensure the synchronization between characteristic variables and load time variations during the prediction process. It is worth noting that in different buildings, the data acquisition and control intervals of the control systems often vary [119]. Therefore, accurately predicting load demand and energy consumption is essential for achieving precise matching between the supply and demand sides of heating, ventilation, and air conditioning systems [120]. As shown in Figure 11, a thermal load forecasting method based on real-time data from smart meters is proposed, utilizing an online correction mechanism to ensure the contextual adaptability of the thermal load prediction model.

Data-driven modeling for heat load prediction is an important strategy in response to energy conservation, emission reduction, and environmental protection needs [121]. By fully integrating data science and information technology, it can significantly enhance the operational efficiency of urban heating systems. Currently, there are various models available for heat load prediction, including but not limited to statistical regression models [122], SVR [123], neural network models [51], and models based on decision trees [124]. These models provide powerful technical support for accurate prediction and efficient management of building heat loads.

Figure 11. Thermal load predict flow chart [125].

Figure 11. Thermal load predict flow chart [125].

3.3.3. Water Load

Water load mainly consists of household water usage [126] and the amount of water used for cooling in thermal storage air-conditioning systems [127]. Understanding the accurate overview of domestic hot water (DHW) usage is crucial for determining the optimal heating strategy for various applications and optimizing the components of the DHW system [128]. DHW consumption is influenced by climate, habits, environmental issues, and socio-economic status, exhibiting significant regional differences. By further considering parameters such as age, occupation, and others, the prediction of the DHW model will become more precise.

Accurate prediction of water load without increasing additional management costs, while maintaining usage comfort, helps to reduce building energy consumption and management costs. The prediction methods for water load are similar to those for thermal load and electrical load and can be achieved using advanced technologies such as deep learning [129,130]. Based on external factors like weather data, occupancy rate, and the day of the week, the amount of water required for cooling can be predicted, ensuring the satisfaction of the building’s cooling needs for the next day while minimizing electricity expenses and carbon emissions.

The specific energy loads of a building will vary depending on factors such as the building’s type, purpose, and scale, as well as the climate and culture of the region it is located in. Therefore, in practical building design and energy management, detailed load calculations and analyses need to be conducted to ensure that the building’s energy supply and usage achieve the goals of efficiency, safety, and economy.

4. Data-Driven Framework for Building Energy Consumption Prediction

Data-driven building energy consumption prediction is a systematic approach that relies on advanced technologies such as historical data, statistical methods, machine learning, and deep learning algorithms to accurately predict the future energy consumption of buildings [131]. The core of this approach lies in the in-depth analysis of vast amounts of data to uncover patterns, trends, and key influencing factors in load changes, thereby significantly enhancing the accuracy of the forecasts. Through this methodology, energy management decisions for buildings can be optimized, leading to energy conservation, emission reduction, and effective control of operational costs. The data-driven framework for building load prediction, as illustrated in Figure 12, primarily encompasses five crucial steps: data acquisition, data preprocessing, feature analysis, model development, and result evaluation. Each step is indispensable, collectively forming an efficient and accurate prediction system.

4.1. Data Acquisition

To accurately predict future energy demand, it is essential to conduct in-depth analysis and extensive collection of historical and real-time data. The aggregation and analysis of these data can uncover the fluctuation patterns of building loads, thereby enhancing the accuracy of predictions. There are three main ways to obtain these data: direct field collection, retrieval of open datasets, and software simulation generation.

Field collection captures building loads and related indicators through precision sensors. In terms of data quality, this method enables the acquisition of high-quality data with high resolution that closely matches the actual situation of specific buildings, accurately reflecting the real-time dynamics of building energy consumption. However, it is extremely costly, involving not only the procurement and installation costs of sensors but also long-term maintenance expenses. Moreover, due to the professionalism of data collection and the limitations of specific locations, the public accessibility is extremely low. During the data collection process, sensor failures or signal transmission problems occur frequently, which necessitate data cleaning, such as filling in missing values and noise reduction, to ensure the availability and accuracy of the data.

As countries implement energy data openness policies to promote the energy efficiency of buildings, the building energy consumption data publicly available from governments and organizations has become an important resource for research. Take the open dataset of urban building energy from 33 cities across 8 countries compiled by Xiaoyu Jin and her team as an example. This dataset covers 13 types of variables, providing valuable references for urban building energy consumption modeling and the development of energy policies [28]. In terms of data quality, such open datasets have a certain degree of standardization and authority after being organized and reviewed. However, in terms of accessibility, due to factors such as the increased difficulty of data collection and privacy protection, the proportion of high-frequency data in open datasets is relatively low. This poses a significant challenge to the short-term building energy consumption prediction work that requires high-frequency data to capture the details of short-term energy consumption changes.

To make up for this deficiency, researchers also use software simulation methods to generate building energy consumption data. Mature software such as EnergyPlus, ESP-r, and TRNSYS can simulate and generate data by inputting the physical parameters and specific equations of buildings. These simulated data play an important role in the initial stage of model training and can be used to initially construct the model framework. However, in terms of data quality, the simulated data are generated based on theoretical models and assumptions after all, and there is a certain deviation from the real-world situation. Subsequently, real-world data are required to iteratively optimize the model to improve its accuracy and reliability. In terms of accessibility, relevant software generally has corresponding acquisition channels. As long as researchers have the corresponding software usage knowledge and hardware conditions, they can relatively easily acquire and use this software to generate data.

Despite the diverse methods of acquiring building energy consumption data, the issues of inconsistent data quality and scarcity of high-frequency data remain challenging. In the context of insufficient data, the application of transfer learning in building energy consumption prediction has been increasing. Grubinger’s online transfer learning framework, for instance, has significantly improved the temperature prediction accuracy of new buildings using only data from the previous few weeks [132]. Ribeiro’s Hephaestus method, on the other hand, enhances the prediction accuracy of buildings with limited data through cross-building energy consumption prediction [133]. Fan et al. [134] proposed that transfer learning can reduce the prediction error in 24-h building energy consumption forecasting by approximately 15% to 18%. Additionally, Liu et al. [135] proposed a transfer learning-based fault diagnosis method for building chillers, significantly improving diagnostic accuracy.

However, while transfer learning can alleviate the issue of insufficient data, it does not directly solve the problem of model accuracy. To enhance the accuracy of prediction models, it is necessary to approach the issue from both model construction and data quality perspectives. This includes adopting more advanced model structures, implementing more refined data preprocessing and feature extraction strategies, and other relevant measures.

4.2. Data Preprocessing

Given the multiple factors such as instrument sensor failures, climate fluctuations, and atypical changes in customer consumption patterns, preprocessing the raw building energy consumption data becomes particularly crucial [136].

During the preprocessing stage, two major challenges need to be addressed urgently: data missingness and data anomalies. Data missingness primarily stems from sensor malfunctions or disruptions caused by unexpected events, resulting in the omission of data samples. These missing values are often marked as “null” in the system or can be identified through the continuity check of timestamps. Common strategies to tackle this issue include deletion, constant imputation, and interpolation. Deletion is suitable for scenarios where the data volume is sufficient, missing values are scarce, and the variable’s sensitivity to time changes is low. Constant imputation involves filling in missing values with the average of the data series or a reasonable constant based on experience [137], particularly useful when numerical fluctuations are small or missing data are concentrated. Interpolation methods, including advanced techniques such as nearest neighbor interpolation, polynomial interpolation, spline interpolation, Kriging, and Lagrange interpolation [138], predict unknown values based on known data points, often yielding higher accuracy than simple constant imputation.

Outliers refer to observations that significantly deviate from the overall data distribution and may negatively impact model learning and prediction performance [139]. Before handling outliers, their existence must first be determined using algorithms like boxplot analysis [140], Z-score testing [16], or isolation forests [141]. Once outliers are confirmed, various methods can be employed to eliminate their adverse effects, including direct deletion, substitution with the upper or lower limits of the data series, or generating replacement data using missing value handling techniques.

Furthermore, data normalization, as a pivotal step in preprocessing, aims to convert all data to a common scale or range, thereby mitigating the interference from irrelevant dimensions and ensuring fair comparisons among features [142]. This process not only helps reduce the disturbance caused by fluctuations in dataset values but also makes the training process more stable and efficient. Common normalization methods include Min-Max normalization and Z-score normalization. Min-Max normalization maps the original data to a specified interval through linear transformation [143], while Z-score normalization transforms the data into a standard normal distribution form. Both methods can effectively enhance the efficiency and accuracy of data processing and analysis.

4.3. Feature Engineering

Feature engineering is a crucial component in data-driven methodologies [144]. By preprocessing and transforming raw data, it constructs features that can effectively capture the essence of the problem, thereby enhancing the predictive performance of models [145]. In the context of building energy consumption prediction, feature engineering holds paramount importance due to the intricate interplay of multi-dimensional and multi-level factors influencing building energy consumption, including meteorological information, indoor environments, occupancy patterns, building characteristics, and socio-economic information [146]. The influencing factors are summarized in

Table 2. The primary features that influence building loads.

Num.	Feature Types	Contents
1	Meteorological information	Outdoor temperature, humidity, wind speed, solar radiation, rain- fall, air pressure, and other factors.
2	Indoor environmental information	Set temperature, indoor temperature, humidity, carbon dioxide concentration.
3	Occupancy-related data	The number of occupants and types of activities.
4	Time indices	Date, day of the week, time of day, and holidays.
5	Building characteristic data	Compactness, surface area, wall area, roof area, height, orientation, glass area, thermal transmittance coefficient, and other factors.
6	Socio-economic information	Income levels, electricity prices, GDP, population.
7	Historical data	Historical meteorological data, historical energy consumption data.

.

Meteorological information [147,148] encompasses outdoor temperature, humidity, wind speed, solar radiation, rainfall, air pressure, and other factors, all of which directly impact the energy demand of buildings. Indoor environmental information [149,150], on the other hand, comprises set temperature, indoor temperature, humidity, carbon dioxide concentration, and so forth, influencing both user comfort and building energy consumption. Occupancy-related data [151,152], such as the number of occupants and types of activities, affect the internal load of buildings. Time indices [153], including date, day of the week, time of day, and holidays, reflect occupancy patterns and activity rhythms. Building characteristic data encompasses compactness, surface area, wall area, roof area, height, orientation, glass area, thermal transmittance coefficient, and other factors, all of which influence the specific energy demand characteristics of buildings [154]. Socio-economic information [155], including income levels, electricity prices, GDP, population, and the like, shapes users’ energy consumption behaviors and, consequently, building energy consumption. Lastly, historical data, comprising historical meteorological data, historical energy consumption data, and the like, reflect the load characteristics of buildings.

The prevalent feature extraction methods include [146]:

(1)

Variable ranking is employed, which involves identifying the most relevant features to the target variable through techniques such as correlation analysis. Notable examples of this approach include the use of the Pearson correlation coefficient and the Spearman rank correlation coefficient.

(2)

There are filtering and wrapper methods, which assess the predictive performance of individual features or subsets of features to determine the optimal feature set. Principal component analysis (PCA) and random forest (RF) are among the commonly used techniques within this category.

(3)

Embedded methods represent another significant approach, wherein feature selection is seamlessly integrated into the learning algorithm itself. Lasso regression is a prime example, as it inherently performs feature selection as part of its regression process.

(4)

Autoencoders (AEs) utilize deep learning techniques to learn nonlinear representations of the input data, thereby effectively extracting useful features. This method leverages the power of neural networks to uncover complex patterns within the data.

Feature engineering plays a pivotal role in building energy consumption prediction. By selecting appropriate feature types and employing effective feature extraction methods, it can significantly enhance the predictive performance of models, reduce their complexity, and improve their interpretability. In the future, as data collection and analysis technologies continue to evolve, feature engineering will become even more crucial, providing more potent tools for building energy consumption predictions.

Through the above feature engineering, a series of factors that have a significant impact on building energy consumption were screened out. These factors constitute important inputs for the prediction model. Different prediction models demonstrate their unique performances precisely based on different ways of handling these factors and the ability to mine the internal patterns of the data. For example, in polynomial regression models, factors such as outdoor temperature and humidity in meteorological information may be used to make predictions by establishing linear or polynomial relationships with energy consumption. In SVR, these factors will be mapped to a high-dimensional space, and the relationship between energy consumption and these factors is fitted by finding the optimal hyperplane. Other factors such as indoor environmental information and living patterns also play a key role in different models. The way they are combined with the model determines the model’s ability to capture the complex patterns of building energy consumption.

4.4. Model for Prediction

In recent years, the field of building energy consumption prediction has witnessed the emergence of various advanced models, with polynomial regression, SVR, MLP, LSTM, Transformers, among others, being particularly prevalent.

4.4.1. Polynomial Regression

Polynomial regression, as a classic method for predicting building loads, primarily takes two forms: univariate and multivariate. Univariate polynomial regression focuses on historical data of building energy consumption, utilizing polynomial equations to forecast future energy consumption trends. Its basic form is:

$$Y=a_1+a_{2}x+a_3{x}^2+\cdots +a_{n+1}{x}^n$$

(1)

where, $Y$ represents the predicted energy consumption value, $x$ represents the historical energy consumption data or a time variable and $a_1$ to $a_n$ are the polynomial coefficients obtained through optimization algorithms such as least squares regression. It is noteworthy that the choice of model order n is crucial. While higher-order models can capture complex trends, they are prone to over-fitting. Conversely, lower-order models may miss important information due to under-fitting. Therefore, in practice, second- or third-order polynomials are often preferred as a balanced choice, with the second-order polynomial model particularly popular due to its moderate complexity and good generalization ability.

Compared to the univariate model, the multivariate version further considers multiple factors that influence building energy consumption, such as temperature, humidity, wind speed, building size, and material properties. The model expression for the multivariate polynomial regression is:

$$Y=a_1+a_2{x}_1+a_3{x}_2+\cdots +a_{n+1}{x}_n$$

(2)

where, $x_1$ to $x_n$ denotes the data of $n$ factors influencing energy consumption, and $a_1$ to $a_n$ are the corresponding coefficients. To enhance the prediction accuracy of the model, researchers also explore feature engineering techniques, such as feature multiplication, higher powers, etc, to capture the nonlinear relationships between variables. Similarly, the estimation of coefficients relies on statistical methods like least squares regression. Chen et al. [156] proposed a physics-based multiple linear regression (PB-MLR) model specifically designed for predicting the hourly cooling loads of buildings. By integrating physical principles with statistical methods, this model demonstrates robust predictive capabilities. Additionally, Ravichandran et al. [157] employed multiple linear regression techniques to investigate the relationship between the geometric forms of residential buildings in India and their cooling loads, providing a scientific foundation for optimizing building design. The advantage of such models lies in their high generalization ability even with small sample sizes, efficient training processes, and strong interpretability of results, which aid decision-makers in intuitively understanding the key factors influencing building energy consumption.

4.4.2. SVR

SVR is a versatile supervised learning approach for regression. Its decision boundary is manifested as the maximal margin hyperplane derived from the learning samples. By incorporating kernel functions, SVR maps the input space into a higher-dimensional feature space, where it seeks an optimal hyperplane for classification or regression prediction. Given a dataset $x$, $y$, where $x\in \left\{x_1,x_2,\cdots ,x_n\right\}$, $y\in \left\{y_1,y_2,\cdots ,y_n\right\}$, $x$ and $y$ represent the input and output spaces, respectively, with n being the number of training data points, SVR formulates the best-fit function as follows:

$$f\left(x\right)=w^T\phi \left(x\right)+b$$

(3)

where $x$ represents the transformed matrix. $W$ and $b$ are estimated by solving an optimization problem, which is defined as follows:

$$\underset{W,b,{\xi }_i^{*},{\xi }_i,}{min}\left\{{\displaystyle \frac{1}{2}}{‖x‖}^2+C\sum _{i=1}^n{\xi }_i^{*}+{\xi }_i\right\}$$

(4)

Subject to

$$\left\{\begin{array}{c}\left(y_i-W^T\phi \left(x_i\right)-b\right)\le \epsilon +{\xi }_i\\ \left(W^T\phi \left(x_i\right)+b-y_i\right)\le \epsilon +{\xi }_i^{*}\\ {\xi }_i\ge 0,{\xi }_i^{*}\ge 0,i=1,\dots ,n\end{array}\right.$$

(5)

where, $C$ is the regularization parameter, ${\xi }_i^{*}$ and ${\xi }_i$ are slack variables that allow flexibility in the constraints.

SVR excels in handling nonlinear, high-dimensional, and small sample data, making it well-suited for complex and data-driven tasks such as building energy consumption predictions. The performance of SVR is largely determined by the choice of kernel function. Commonly used kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel, among others. In building energy consumption prediction, the RBF kernel is frequently selected due to its excellent nonlinear mapping capabilities [158,159]. SVR is capable of addressing the nonlinear relationships between input and output variables, rendering it suitable for complex problems like building energy consumption prediction [160]. By selecting an appropriate kernel function and tuning parameters, SVR models can achieve high prediction accuracy. During the training process, SVR models seek the optimal hyperplane by maximizing the margin, a strategy that not only enhances the model’s fitting ability on training data but also imparts robust generalization performance on unseen data, ensuring the stability and consistency of prediction results in practical applications.

4.4.3. MLP

Among the diverse types of ANN, MLP has gained widespread adoption in the field of predicting building energy consumption. As a pivotal branch of ANN, MLP is renowned for its formidable capability in approximating nonlinear functions, enabling it to simulate the dynamic behaviors of complex systems with high precision, all without relying on intricate explicit model construction [161]. The structure of MLP is illustrated in Figure 13.

MLP consists of an input layer, hidden layers, and an output layer. The input layer receives input variables. Let $u\left(t\right)$ represent the system’s output at time t, and τ1, τ2, …, τn denote the time constant. The input layer is connected to the hidden layers, and the hidden layers are connected to the output layer through weights. After initializing these weights, the information propagates forward through the neurons upon activation [162]. By adjusting its internal connections and weights, the MLP Neural Network is able to deeply mine the hidden patterns within input data, thereby facilitating precise learning and prediction and providing robust technical support for modeling complex systems and solving related problems.

To accurately predict the cooling water load required by commercial buildings, Sanzana et al. [127] developed a machine learning model based on the MLP. This model ingeniously integrates key input variables such as the weather forecast for the following day, the day of the week, and the building occupancy rate. Unlike traditional prediction methods, this model sets its prediction target as a specific range of water volumes, offering facility managers a set of practical and flexible reference options that facilitate decision-making based on actual conditions. Furthermore, Afzal et al. [31] leveraged this MLP Neural Network framework to further explore its potential in predicting both cooling and heating loads in buildings. Among the multitude of potential solutions, MLP was selected as the core algorithm for predicting building energy consumption. Experimental data has demonstrated that, compared to other methods, the MLP Neural Network exhibits superior prediction accuracy, ushering in new breakthroughs and possibilities in the field of building energy consumption prediction.

4.4.4. LSTM

Deep learning algorithms, compared to traditional predictive algorithms such as ANN and SVR, exhibit a more intricate structural framework. This characteristic endows them with the capability to uncover deeper, more intricate relationships between building energy loads and their associated variables, resulting in a notable enhancement in prediction accuracy under specific conditions. Numerous studies have corroborated this viewpoint: Mocanu et al. [41] found that factorized conditional restricted boltzmann machines (FCRBMs) outperformed other algorithms in terms of prediction precision when comparing various deep learning and traditional machine learning algorithms for electric load forecasting. Meanwhile, Koschwitz et al. [163] demonstrated the advantages of RNN in heating and cooling load predictions compared to SVR. LSTM networks, as the preeminent deep learning approach for handling time-series data, have further elevated predictive performance by addressing the common issues of gradient vanishing or exploding encountered in RNNs [164]. The schematic diagram of the information flow in LSTM networks is shown in Figure 14. Muhammad Faiq et al. [165] demonstrated the precision of LSTM models in predicting daily energy consumption in institutional buildings, verifying LSTM’s excellence in reducing RMSE compared to SVR and Gaussian process regression (GPR). Additionally, LSTM variants such as coupled input and forget gate (CIFG) and gated recurrent unit (GRU) have been widely applied and validated in practice. RNNs have unique advantages in processing time-series data, and the introduction of the gating mechanism further enhances the performance of RNNs, making them more suitable for tasks such as building energy consumption predictions that require a high degree of time-series dependence. LSTM, as a variant of RNN, effectively solves the problems of gradient vanishing or explosion in traditional RNN by introducing input gates, forget gates, and output gates. The input gate determines how much of the current input information will be added to the memory cell; the forget gate controls how much of the information in the memory cell from the previous time step will be retained to the current time step; and the output gate decides how much of the information in the memory cell will be output for the prediction at the current time step. For example, in building energy consumption prediction, LSTM can use these gating mechanisms to dynamically adjust the degree of memory and forgetting of information at different time steps based on historical energy consumption data and current influencing factors (such as meteorological data, building usage, etc.), thus more accurately capturing the long-term dependencies of energy consumption data.

To further elevate prediction accuracy and overcome the limitations of single technologies, researchers have explored the application of hybrid strategies. A novel hybrid model integrating bidirectional long short-term memory (Bi-LSTM), CNN, and grey wolf optimizer (GWO) has been proposed. In this model, GWO optimizes the parameters of CNN and Bi-LSTM, while one-dimensional CNN effectively captures the features of time-series data. Validated using data from four buildings with distinct characteristics, this model has exhibited outstanding predictive performance [167].

Moreover, to tackle the challenge of over-fitting, Kong et al. [168] developed a composite framework based on LSTM and feedforward neural networks (FFNNs), specializing in individual consumer energy forecasting, which effectively improved prediction accuracy. Additional research has devised nine distinct deep learning frameworks for forecasting the energy demand of five buildings located in various geographical regions, from one hour to one day ahead, showcasing the wide applicability and robustness of deep learning technologies in diverse building energy prediction scenarios [169]. In the realm of residential energy consumption prediction, the combination of CNN with LSTM and GRU has also yielded remarkable results. Somu et al. [170] further expanded the application boundaries of deep learning in building energy load forecasting by incorporating the K-Nearest Neighbors algorithm into a CNN-LSTM hybrid technique specifically designed for academic building energy predictions.

Deep learning technologies have demonstrated immense potential and advantages in building energy load forecasting. Their sophisticated algorithmic structures enable models to capture more complex and nuanced data relationships than traditional methods, thereby significantly enhancing prediction accuracy. By combining diverse deep learning frameworks, researchers have overcome the limitations of single technologies and introduced various hybrid prediction models that exhibit good adaptability and robustness when dealing with building energy data with different characteristics and application scenarios. As deep learning technologies continue to evolve and mature, we have every reason to believe that they will play an increasingly important role in building energy management, energy conservation, and emission reduction, as well as smart grid construction, contributing significantly to the promotion of green, low-carbon, and sustainable energy development.

4.4.5. Transformer

The cornerstone of the Transformer model lies in its distinctive self-attention mechanism, which adeptly captures intricate dependencies among vectors at any position within the input sequence, transcending the limitations of traditional sequential models in processing temporal information. Not only does the Transformer facilitate efficient parallel training, boosting model training speed to unprecedented levels, but it also effectively preserves the temporal characteristics of sequences through deep learning capabilities, ensuring high-precision predictions. Since its inception, the Transformer model has swiftly garnered widespread attention from researchers due to its remarkable performance and flexibility, particularly demonstrating immense potential in the field of building energy load forecasting. Figure 15 shows a Transformer model used for building load forecasting [171].

As shown in Figure 15, a transformer model specifically designed for predicting the cooling loads of commercial buildings has been developed. This model harnesses the power of the self-attention mechanism to precisely model long-range dependencies between input and output sequences, effectively capturing and retaining crucial information from long-term time series, thereby providing a more comprehensive and insightful basis for cooling load predictions [172].

Ni et al. [173] have further broadened the application scope of the transformer by introducing it into the realm of multi-level building energy forecasting. They compared various strategies, including seven deep learning methods, with the temporal fusion transformer (TFT) model emerging as the standout performer, exhibiting exceptional capabilities in both point and probabilistic forecasting. Through empirical studies conducted on two different building types (a Swedish city museum and a theater), they validated the high predictability of their developed model across diverse energy consumption scenarios. Wang et al. [174] proposed an innovative multi-task energy load forecasting model based on the Transformer architecture. This model employs a multi-decoder framework that utilizes multi-head attention mechanisms to focus on the encoder’s output representations at various levels. This approach significantly enhances both the accuracy and efficiency of simultaneous predictions for multiple energy loads.

Furthermore, Gao et al. [175] have contributed by proposing an interpretable deep learning model that integrates LSTM and self-attention mechanisms. By introducing a dual attention mechanism based on hidden states and features, they have enhanced the transparency and credibility of the model in building energy consumption forecasting. They validated the effectiveness and practicality of their model through a case study involving an office building.

Collectively, these studies have not only deepened the application of the Transformer model in the field of building energy but also provided robust technical support for future intelligent energy management and optimization.

4.4.6. K-Nearest Neighbors

The K-nearest neighbors (KNN) algorithm is a simple instance-based machine learning algorithm. In the scenario of building energy consumption prediction, for a building energy consumption sample to be predicted, the KNN algorithm will find the K samples in the training dataset that are most similar in features to this sample (usually measured by Euclidean distance, Manhattan distance, etc.). Then, it predicts the energy consumption of the target sample based on the energy consumption values of these K neighbor samples. For example, if K is set to 5, it will find the 5 samples closest to the sample to be predicted. If the energy consumption of 3 out of these 5 samples is within a certain range, then the energy consumption of the sample to be predicted is likely to be predicted within that range as well.

The advantages of the KNN algorithm are its simplicity and intuitiveness. It does not require a complex training process, and the model update is easy, only needing to add new training samples. However, it has a high computational complexity, as it needs to traverse the entire training dataset to calculate the distances during prediction. Moreover, the selection of the K value has a significant impact on the prediction results. If the K value is too small, the model is sensitive to noise; if the K value is too large, the model may become vague and unable to accurately reflect local features. In building energy consumption prediction, it is necessary to select an appropriate K value according to the characteristics of the specific dataset and prediction requirements through methods such as cross-validation to improve the prediction accuracy.

4.4.7. CNN

CNNs were initially mainly applied in the field of image recognition, but in recent years, they have gradually been used in tasks of processing time-series data, such as building energy consumption prediction. CNN automatically extracts data features through components such as convolutional layers, pooling layers, and fully connected layers.

When processing building energy consumption time-series data, the convolutional kernels in the convolutional layer can be regarded as small filters that slide over the data and extract local features in the data through convolution operations, such as the change patterns of energy consumption in different time periods. The pooling layer is used to reduce the data dimension, decrease the amount of calculation, and retain important features at the same time. After multiple layers of convolution and pooling operations, the data are input into the fully connected layer for the final prediction.

For example, when predicting the energy consumption of residential buildings, CNN can predict future energy consumption by learning the local patterns in historical energy consumption data and related influencing factors (such as weather data). Compared with traditional machine learning algorithms, CNN can automatically learn the features in the data without the need for manual feature design, which has great advantages in processing complex building energy consumption data. In addition, CNN has translational invariance, has good adaptability to local feature changes in the data, and can more accurately capture the patterns in building energy consumption data. To better apply it to building energy consumption prediction, researchers will also improve the CNN structure, such as combining it with the Long Short-Term Memory network (LSTM) and so on, to better handle the long-term dependencies in time-series data.

4.4.8. Tree-Based Models

Tree-based models are widely used in building energy consumption prediction, especially in the field of energy load forecasting dealing with datasets with a large amount of zero inflation.

The decision tree is the basis of tree-based models. It constructs a tree structure for prediction by recursively dividing data features. In building energy consumption prediction, the decision tree can divide the data space into different regions according to various factors such as meteorological information (such as temperature and humidity) and building features (such as building orientation and area), and each region corresponds to a specific energy consumption prediction value.

As an ensemble extension of the decision tree, the random forest shows powerful performance in building energy consumption prediction. It is composed of multiple decision trees, and the final prediction is obtained by synthesizing the prediction results of multiple decision trees (usually by voting or averaging). During the training process, the random forest randomly samples multiple subsets with replacement from the original training dataset, and each subset is used to train a decision tree. Moreover, in the process of node splitting when constructing a decision tree, instead of considering all features, only a part of the features is randomly selected to find the best splitting point. This randomization strategy makes the random forest have good generalization ability and can effectively avoid overfitting.

For example, Olu-Ajayi et al. [32] found that the random forest showed certain advantages in handling complex data relationships when comparing the performance of various building energy consumption prediction models, and it can capture the non-linear relationships among various factors affecting building energy consumption. It has a certain degree of robustness to outliers and noisy data because the wrong prediction of a single decision tree will not dominate the final result. At the same time, the random forest has a relatively fast training speed and can be processed in parallel, which is beneficial for the analysis of large-scale building energy consumption data. In practical applications, by adjusting hyperparameters such as the number of decision trees and the number of features considered during node splitting, the performance of the random forest model in building energy consumption prediction tasks can be further optimized.

The gradient boosting tree (GBT) is also an important tree-based model. It iteratively trains a series of weak learners (usually decision trees), and each iteration learns based on the residuals of the previous model, thus gradually improving the prediction ability of the model. In building energy consumption prediction, GBT can continuously fit the complex patterns in the data. For datasets with a large amount of zero inflation, it can improve the prediction accuracy by gradually optimizing the data in different regions.

For example, when dealing with a large number of zero-energy consumption periods (such as during holidays or when equipment is shut down) in some building energy consumption data, GBT can specifically learn the relationship between the features of these periods and energy consumption so as to more accurately predict future energy consumption situations.

4.4.9. Ensemble Models

Ensemble models combine multiple different models to make full use of the advantages of each model and improve the overall prediction performance. In building energy consumption prediction, common ensemble strategies include Bagging and Boosting.

Bootstrap aggregating (Bagging) is a parallel ensemble method, and the random forest is a typical representative based on the Bagging strategy. It generates multiple sub-datasets by sampling with replacement from the original dataset and then trains different models (such as decision trees) on these sub-datasets, respectively. Finally, the prediction results of these models are synthesized (voting is usually used for classification tasks, and averaging is usually used for regression tasks). Bagging can reduce the variance of the model, improve the stability and generalization ability of the model, and have a good effect on processing complex building energy consumption data.

Boosting is a sequential ensemble method. It trains multiple models in turn, and each model is improved based on the errors of the previous model. In addition to the Gradient Boosting Tree mentioned above, Adaptive Boosting (Adaboost) is also a commonly used Boosting algorithm. In building energy consumption prediction, Adaboost first assigns an initial weight to each training sample and then trains the first model. According to the prediction results of the first model, the weights of the samples are adjusted so that the weights of the misclassified samples are increased and the weights of the correctly classified samples are decreased. Then, the second model is trained based on the adjusted weights, and so on. Finally, the prediction results of all models are linearly combined according to certain weights to obtain the final prediction result. The Boosting method can improve the accuracy of the model, but it may be more sensitive to noisy data, so appropriate data preprocessing is required when using it.

4.5. Evaluation Metrics

The evaluation metrics of predictive models serve as indispensable quantitative tools for assessing their performance and effectiveness. By precisely measuring the model’s performance on a test set, they provide researchers with clear benchmarks for evaluating the merits of different models [176]. Among the numerous evaluation metrics, mean absolute error (MAE), MAPE, mean square error (MSE), RMSE, and R² score are frequently used [177].

$$MAE={\displaystyle \frac{1}{n}}\sum _{i=1}^n\left|x_i-y_i\right|$$

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$$MAPE={\displaystyle \frac{100\%}{n}}\sum _{i=1}^n\left|{\displaystyle \frac{y_i-x_i}{y_i}}\right|$$

(7)

$$MSE={\displaystyle \frac{1}{n}}\sum _{i=1}^n{\left(x_i-y_i\right)}^2$$

(8)

$$RMSE=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{(y_i-x_i)}^2}{n}}}$$

(9)

$$R^2=1-{\displaystyle \frac{{\sum }_{i=1}^n{\left(y_i-x_i\right)}^2}{{\sum }_{i=1}^n{\left(y_i-\overline{y}\right)}^2}}$$

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where n represents the number of samples; x_i denotes the i-th simulated value; y_i indicates the i-th actual measurement; $\overline{x}$ represents the mean of the simulated values; and $\overline{y}$ signifies the mean of the actual measurements.

In building energy consumption prediction, different algorithms are closely related to these evaluation metrics. Take the linear regression algorithm as an example. It attempts to find the best-fitting straight line to describe the relationship between energy consumption data and related variables. In this case, the MAE can intuitively reflect the average degree of deviation between the predicted values of linear regression and the actual building energy consumption values. Since linear regression is sensitive to the overall trend of the data, the MAE can robustly measure its prediction error. Even if there are a few abnormal energy consumption data points, they will not overly interfere with the judgment of the overall error of the model. However, when linear regression encounters complex non-linear relationships in the data, the MAE may not be able to comprehensively reflect the differences in the prediction performance of the model in different regions. At this time, combining it with the MSE or RMSE can further analyze the situation.

The decision tree algorithm classifies or predicts building energy consumption by constructing a tree structure. The MAPE is of great significance for the evaluation of the decision tree algorithm. Because when the decision tree processes different categories of data, the MAPE can intuitively display the relative prediction error in the form of a percentage, helping researchers understand the accuracy of the decision tree’s prediction within different energy consumption level intervals. However, the decision tree may change greatly due to slight fluctuations in the data when dividing nodes. If there are outliers in the data, the MAPE will be significantly increased, leading to a deviation in the evaluation of the overall performance of the model.

The neural network algorithm is widely used in building energy consumption predictions, and its complex structure enables it to learn complex patterns in the data. When evaluating a neural network model, the MSE, due to its amplifying effect on larger errors, can prompt the neural network to pay more attention to those energy consumption data points with larger prediction deviations during the training process. Through the backpropagation algorithm, the network parameters are continuously adjusted to improve the overall prediction accuracy. As the square root of the MSE, the RMSE provides a more intuitive interpretation of the prediction results of the neural network model. Researchers can quickly determine the degree of closeness between the predicted values of the neural network model and the actual building energy consumption values through the RMSE and then evaluate the performance of the model.

The R² value is universal in the evaluation of various algorithms. For the above-mentioned linear regression, decision tree, and neural network algorithms, the closer the R² value is to 1, the better the fitting effect of the model on the building energy consumption data, that is, the higher the proportion of the variance of the energy consumption data that the model can explain. For example, in a neural network model, the R² value can help researchers determine whether the network structure is reasonable and whether it has fully learned the internal laws in the energy consumption data.

In terms of evaluation strategies, a single indicator often cannot comprehensively evaluate the performance of a building energy consumption prediction model. For example, relying solely on the MAE may ignore the prediction differences of the model in different data distribution regions; while using the MAPE alone, due to its sensitivity to outliers, the overall performance of the model may be misjudged due to individual abnormal energy consumption data. Therefore, it is usually recommended to use a combination of multiple indicators for evaluation. For instance, using both the MAE and the RMSE at the same time, the MAE provides a robust estimate of the error, and the RMSE more intuitively reflects the degree of closeness between the predicted values and the true values, comprehensively evaluating the model performance from different angles. In addition, methods such as cross-validation can also be used. The dataset is divided into multiple subsets, and training and testing are carried out on different subsets to reduce the evaluation bias caused by the division of the dataset so as to more accurately evaluate the generalization ability of the model in building energy consumption prediction.

5. Discussion

5.1. Opportunities for Further Works

The data-driven approach is spearheading profound transformations in the field of building energy consumption prediction. This methodology not only delves deeply into the inherent logic and intricate mechanisms of energy consumption data but also provides a solid scientific foundation for precise prediction of building loads, optimization of energy allocation, and enhancement of energy efficiency levels [178,179]. As such, it has emerged as a pivotal driving force propelling research and innovation in the field of building energy towards greater heights. Looking ahead, the research directions for building energy consumption prediction will become increasingly diversified and cutting-edge, encompassing the following:

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Multi-agent collaboration for energy load forecasting and optimization. Confronted with the complex energy systems composed of diverse building types within cities or industrial parks, the intricate interplay among various energy loads poses unprecedented challenges for accurately forecasting overall energy loads. The multi-agent collaboration approach, with its distinct advantages of distributed processing, adaptability, and efficient coordination, offers an innovative solution to this dilemma. By simulating the behavioral patterns of different buildings as independent agents, this method can capture and process the dynamic interactions among buildings and with the external environment, thereby achieving high-precision forecasting and intelligent optimization of the regional comprehensive energy loads, providing robust support for energy management decision-making.

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Optimization and efficiency enhancement of deep learning model frameworks in building energy consumption prediction. Deep learning models, with their formidable data-fitting capabilities, have demonstrated immense potential in building energy consumption predictions. However, their high demands for training data volume and quality, as well as the substantial computational resources required, constitute bottlenecks in practical applications. Consequently, future research should concentrate on refining deep learning model frameworks, aiming to strike an optimal balance between computational efficiency and prediction accuracy with minimal computational resources. This encompasses strategies such as designing lightweight network structures, exploring efficient training algorithms, and optimizing data preprocessing procedures, with the goal of maintaining or even enhancing prediction accuracy and timeliness while reducing resource consumption.

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Exploration of generative pre-trained models in building energy consumption prediction. As large language models proliferate across industries, their robust semantic understanding and generation capabilities have sparked new insights into the realm of time-series analysis [180]. Particularly in building energy consumption prediction [181], the introduction and targeted fine-tuning of generative pre-trained models can harness their extensive context awareness and vast prior knowledge to uncover hidden complex patterns and trends within load data. This approach excels at capturing the nonlinear characteristics of load variations more precisely, enhancing the robustness and generalization capabilities of prediction models, thereby charting a promising new avenue for future research in building energy consumption prediction.

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Multi-source data fusion for building energy consumption forecasting. With the advancement of Internet of Things (IoT) and smart sensor technologies, building energy consumption data can originate from multiple sources, including building management systems, smart meters, and weather stations [182]. These data sources vary in terms of temporal resolution, spatial granularity, and quality levels [183]. Future research should focus on effectively merging these diverse data sources to improve the accuracy and reliability of forecasts. This may involve the development of data cleaning, matching, transformation, and fusion algorithms.

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Edge computing has important applications in building energy consumption prediction. Edge computing is capable of performing data processing at devices close to data sources or at the network edge, reducing data transmission latency and increasing data processing efficiency. In the context of building energy consumption prediction, by deploying edge-computing nodes on intelligent devices within buildings, the locally-collected energy-consumption data can be analyzed in real-time. This not only enables a rapid response to real-time changes in building energy consumption and the timely adjustment of prediction-model parameters but also eases the burden on the cloud-computing center and safeguards data privacy. For example, edge computing can perform preliminary processing on the high-frequency energy-consumption data generated in real-time by smart meters. After extracting key features, the data are then transmitted to the cloud for in-depth analysis, thus enhancing the timeliness and accuracy of the overall prediction.

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Blockchain technology plays a facilitating role in the management of building energy consumption data. Blockchain, with its characteristics of decentralization, immutability, and traceability, provides new ideas for the management of building energy consumption data. In the field of building energy-consumption prediction, data from different sources may have trust-related issues, and blockchain can ensure data authenticity and integrity. For instance, through blockchain technology, the energy-consumption data collected from building management systems, smart meters, and other devices is recorded. The entry of each data item requires verification by multiple nodes, ensuring that the data cannot be maliciously tampered with. This helps to improve the credibility of data when integrating multi-source data, laying a foundation for constructing a more reliable energy-consumption prediction model. Meanwhile, the smart-contract function of blockchain can also be used to automate the sharing and trading of energy-consumption data, promoting data circulation and rational utilization.

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There is potential to be tapped for crowdsourced data in building energy consumption prediction. Crowdsourced data, sourced from a large number of users, has advantages such as a large volume of data and wide coverage. In building energy-consumption prediction, information about building usage habits and users’ perception of environmental comfort can be collected through crowdsourcing platforms. These data can supplement the deficiencies of data collected by traditional sensors and reveal the influencing factors of building energy consumption from the perspective of user behavior. For example, users can upload information such as their indoor temperature-adjustment preferences and the frequency of electrical-appliance use in the building through a mobile application. Combining these crowdsourced data with the actual energy-consumption data of the building can lead to a more comprehensive understanding of the complex patterns of building energy consumption, thereby optimizing the energy-consumption prediction model and improving the accuracy and comprehensiveness of the prediction.

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Interpretability and transparency in building energy consumption forecasting. The pervasive use of deep learning models for building energy consumption forecasting introduces challenges related to interpretability and transparency due to their complexity and “black box” nature [184]. Future research can explore methods to enhance the interpretability of deep learning models, making their predictions more transparent and trustworthy. For instance, investigations could focus on employing interpretable machine learning techniques such as LIME and SHAP to elucidate deep learning model predictions, thereby aiding building managers in understanding the decision-making processes of these models. Moreover, research could aim to design deep learning models that inherently possess explanatory capabilities, automatically generating interpretive information during the forecasting process.

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Real-time and dynamic building energy consumption forecasting. As energy systems evolve to become increasingly complex and dynamic, building energy consumption forecasts must not only achieve high precision but also exhibit real-time capabilities and dynamic adjustment abilities [185]. Future research could explore strategies for delivering real-time energy consumption forecasts and dynamically adjusting model parameters to account for variations in building usage patterns and environmental conditions [186]. For example, researchers could investigate the application of online and incremental learning techniques to enable models to update and adjust dynamically based on real-time data. Additionally, research could integrate reinforcement learning techniques to allow models to continually optimize their forecasting strategies during real-time prediction.

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Collaborative optimization of building energy consumption forecasting and smart grids. The synergistic optimization of building energy consumption forecasting and smart grid operations is vital for achieving efficient energy utilization [187]. Future research could examine methods to integrate building energy consumption forecasting with the scheduling and optimization processes of smart grids, realizing collaborative synergies between buildings and the grid [188]. For example, researchers could explore how to leverage building energy consumption forecasts to optimize grid load scheduling, mitigating peak loads and fluctuations within the grid. Furthermore, investigations could focus on combining demand response technologies, enabling buildings to adapt dynamically to grid load conditions for mutually beneficial outcomes.

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Incorporating renewable energy fluctuations and uncertainties in building energy consumption forecasting [189]. As the utilization of renewable energy sources within buildings becomes more prevalent, building energy consumption forecasting must account for the inherent variability and uncertainties associated with renewable energy supplies [190]. Future research could explore methodologies for integrating building energy consumption forecasts with renewable energy predictions, facilitating the collaborative optimization of energy consumption and renewable energy utilization [191]. For example, investigations could focus on optimizing the use of renewable energy based on building energy consumption forecasts to reduce reliance on traditional energy sources. Additionally, research could examine the integration of energy storage technologies, enabling buildings to utilize storage devices to supplement energy during periods of insufficient renewable energy supply.

5.2. Challenges

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In practical applications, data-related issues pose numerous challenges to building energy consumption prediction. In terms of data quality and integrity, data often suffers from noise, missing values, and inconsistent formats. For example, sensor failures can lead to data loss, and differences in device data formats impede integration. There is an urgent need for efficient data cleaning and preprocessing techniques. Data privacy and security are also crucial. As the data encompasses more information, preventing data leakage and safeguarding user privacy while achieving effective energy consumption prediction during data sharing and transmission is a difficult problem. For instance, when cloud platforms process data, strict control over encryption and access rights is required. Moreover, multi-source data integration is extremely challenging. Data from different sources vary greatly in time resolution, spatial granularity, and quality. For example, meteorological data and smart meter data have different time scales. How to match and integrate them to improve prediction accuracy remains to be further studied.

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Model-related challenges restrict the practical application of building energy consumption prediction models. Regarding model complexity and interpretability, although deep-learning models are accurate in prediction, their structures are complex, and they are in a “black-box” state. Building managers and energy professionals find it difficult to understand their decision-making processes. For example, the Transformer model has poor interpretability when used for commercial building cooling load prediction. Therefore, developing interpretable deep-learning models or combining interpretable technologies is an important direction. The adaptability and generalization ability of models are insufficient. Different buildings vary in structure, usage patterns, and environments, and existing models struggle to quickly adapt to different scenarios. For example, a model for northern residential buildings cannot be directly applied to southern commercial buildings. The real-time performance and dynamic adjustment of models also urgently need improvement. The dynamic nature of the energy system requires models to be updated rapidly based on real-time data to respond to changes in building usage patterns and the environment. For example, when there is a sudden increase in personnel activities leading to a rise in energy consumption, the model needs to adjust the prediction results in real-time.

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Multiple factors in the actual application environment pose challenges to building energy consumption prediction. The building operation environment is constantly changing. Equipment aging, renovation, and changes in user behavior affect energy consumption characteristics, and existing models have difficulty capturing these in real-time. For example, the prediction accuracy of the model is easily affected when energy-saving lighting equipment is replaced or air-conditioning usage habits change. Policy, regulatory, and market factors cannot be ignored. Changes in energy policies and regulations and fluctuations in energy market prices can change building energy consumption patterns, but these are often not fully considered in models. For example, when an energy-saving subsidy policy is introduced, it needs to be incorporated into the model to conform to reality. In addition, the coordinated optimization of multiple systems is difficult. Building energy consumption prediction needs to be coordinated with smart grids and regional energy systems, but the coordination mechanisms among systems are complex and involve multiple stakeholders. For example, how to adjust power consumption strategies reasonably based on building energy consumption predictions during peak grid loads to achieve a win-win situation is a key challenge.

5.3. Comparative Analysis and Application Considerations of Prediction Models

In building energy consumption prediction, polynomial regression shows its advantages with its simple and intuitive model form. Whether it is the univariate form that focuses on historical energy consumption data to predict trends or the multivariate form that incorporates various influencing factors such as temperature and humidity, it enables decision-makers to easily understand the relationship between variables and energy consumption prediction values. It has good generalization ability in small samples and is efficient in training. However, this model has a limited ability to capture complex non-linear relationships, and it is difficult to select the model order. If the order is too high, overfitting is likely to occur, and if it is too low, underfitting will happen. It is suitable for scenarios where the building structure and usage pattern are simple, the data features are approximately linear or low-degree non-linear, and high interpretability of the model is required, such as energy consumption prediction for small-scale conventional buildings.

SVR maps the input space to a high-dimensional space with kernel functions. Its powerful non-linear processing ability enables it to effectively deal with complex non-linear problems in building energy consumption. It performs well in small-sample learning. By maximizing the margin to find the optimal hyperplane, it endows the model with good generalization performance. However, it has the problem of high computational complexity. When calculating in a high-dimensional space and processing large-scale data, the training time is long, and the requirements for computing resources are high. At the same time, it is sensitive to parameter selection and requires a large number of experiments to determine the optimal parameters. It is suitable for building energy consumption prediction where the data are non-linear, the samples are limited, and high prediction accuracy is required, such as the energy consumption prediction scenarios for buildings with unique designs or complex usage patterns.

As an important branch of artificial neural networks, MLP has a strong non-linear approximation ability. Without the need for complex explicit model construction, it can simulate the dynamic behavior of complex systems such as building energy consumption with high precision through multi-layer neurons and weight adjustments and deeply mine the hidden patterns in the input data to achieve accurate prediction. However, it is prone to overfitting, especially when the training data are insufficient, the parameter setting is improper, or the model structure is complex. Moreover, the training time is long, and the adjustment of a large number of weight parameters involves a large amount of calculation. At the same time, as a black-box model, it lacks interpretability. It is suitable for scenarios with large amounts of data, complex building energy consumption characteristics, high requirements for prediction accuracy, and low requirements for interpretability, such as energy consumption prediction for large commercial complexes or comprehensive office buildings.

LSTM has significant advantages in processing building energy consumption time series data. Its innovative input gate, forget gate, and output gate mechanisms successfully solve the problems of gradient vanishing or explosion in traditional recurrent neural networks and can accurately capture the long-term dependencies in time series. In scenarios such as predicting the daily energy consumption of buildings, it can dynamically adjust the memory and forgetting of information at different time steps based on historical energy consumption and current influencing factors, significantly improving the prediction accuracy and reducing indicators such as root mean square error. Its variants have also been widely applied and verified in practice. However, LSTM has a high computational complexity, and its complex model structure leads to high requirements for computing resources in training and prediction. Moreover, the training is difficult and requires a large amount of data and time for parameter tuning. It is suitable for long-term and accurate prediction of building energy consumption time series, especially for buildings where energy consumption is greatly affected by time factors and long-term trends and seasonal changes need to be captured, such as energy consumption prediction for large public buildings or industrial buildings.

Based on the unique self-attention mechanism, the Transformer can cleverly capture the complex dependencies between vectors at any position in the input sequence, breaking through the limitations of traditional sequential models in processing time information. The efficient parallel training greatly improves the training speed, showing great potential in the field of building energy consumption load prediction. It has been successfully applied to the prediction of cooling loads in commercial buildings, energy consumption in multi-story buildings, etc., and has excellent performance in point prediction and probability prediction. However, it has high requirements for the amount of data. When the data are insufficient, it is difficult to give full play to its advantages and may even have fitting problems. Moreover, the model is complex and belongs to a black-box model, making it difficult to understand and interpret the decision-making process. It is suitable for scenarios with large-scale and high-quality building energy consumption data and high requirements for prediction accuracy and computational efficiency, such as the overall prediction of urban building energy consumption or the fine-grained management prediction tasks of large building clusters.

The KNN algorithm in building energy consumption prediction has a simple and intuitive principle. There is no need for a complex training process, and the model can be updated by adding new samples, which is easy to understand and operate. However, this algorithm needs to traverse the entire training dataset to calculate distances during prediction, resulting in high computational complexity. In large-scale datasets, the prediction speed is slow, and the selection of the K value has a great impact on the prediction result. If the K value is too small, the model is sensitive to noise, and if it is too large, the model becomes fuzzy. It is necessary to carefully select the K value through methods such as cross-validation. It is suitable for scenarios with a small dataset scale, low requirements for real-time performance, and the expectation of quickly obtaining simple prediction results, such as the preliminary exploration of energy consumption prediction for small buildings with limited data.

CNN was initially used for image recognition and now has emerged in the field of building energy consumption prediction. Through components such as convolutional layers and pooling layers, it can automatically learn data features without manual design, effectively mining the local patterns in building energy consumption data, such as the energy consumption change patterns in different time periods. It also has translation invariance, has good adaptability to local feature changes, and can accurately capture data patterns. However, CNN has a limited ability to handle long-term dependencies in time series, which is weaker than specialized time series models. Moreover, in order to be better applied to building energy consumption prediction, it often needs to be combined with other models such as LSTM, increasing the complexity of model structure adjustment. It is suitable for scenarios where building energy consumption data has obvious local features and patterns and has low requirements for long-term dependencies in time series, such as short-term prediction of building energy consumption within a specific time period or energy consumption feature analysis and prediction for a group of buildings with similar building structures and usage patterns.

Tree-based models represented by Random Forest in building energy consumption prediction can effectively capture the non-linear relationships among various influencing factors. By training multiple decision trees through sampling with replacement from the original training dataset and randomly selecting some features during node splitting, they have good generalization ability, effectively avoid overfitting, have a certain degree of robustness to outliers and noisy data, and the error of a single decision tree does not dominate the final result. Moreover, the training speed is relatively fast and can be processed in parallel, which is suitable for large-scale building energy consumption data analysis. However, as a black-box model, it lacks interpretability. Although the importance of features can be analyzed to understand the influence degree of factors, it is still difficult to intuitively explain the decision-making process. At the same time, it is relatively dependent on data feature selection and data balance. Unreasonable feature selection or severely unbalanced data will affect the model performance. It is suitable for processing large-scale and complex building energy consumption datasets and scenarios with high requirements for model generalization ability and robustness, such as energy consumption prediction for large communities or industrial parks with a mixture of different types of buildings.

As an ensemble model strategy, Bagging generates multiple sub-datasets through sampling with replacement from the original dataset to train different models and then synthesizes the prediction results. It can effectively reduce the model variance and improve stability and generalization ability. Each sub-model can be trained in parallel, greatly shortening the training time. It is suitable for building energy consumption prediction scenarios where the variance of the basic model is large and high requirements for model stability and generalization ability are required, such as the comprehensive prediction of energy consumption of various different types of buildings. Boosting, on the other hand, trains multiple models in sequence. Each model is improved based on the errors of the previous model, gradually improving the prediction ability. For example, Adaboost can adaptively adjust the sample weights to improve the overall prediction performance. However, it is sensitive to noise, and noisy data may be amplified during iteration. Moreover, the serial training method leads to a long training time. It is suitable for building energy consumption prediction scenarios with extremely high requirements for prediction accuracy, high data quality, and little noise, such as the energy consumption prediction for special buildings (such as key areas of hospitals, data centers, etc.) with strict control over energy consumption and extremely high accuracy requirements.

5.4. Multi-Step Prediction Strategies and Performance Analysis in Different Building Energy Consumption Prediction Models

In the field of building energy consumption prediction, multi-step prediction strategies play a crucial role in accurately grasping the energy consumption change trends in the future for a certain period. Common multi-step prediction strategies include direct, recursive/rolling, and sequential strategies, and different prediction models exhibit diverse performance under these strategies.

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Direct strategy

The direct strategy means that the model directly predicts the building’s energy consumption for multiple future time steps. Taking the polynomial regression model as an example, it constructs a polynomial equation with multiple future time-step outputs, integrates historical energy consumption and relevant influencing factor data, and can predict the energy consumption at multiple future time points at once. When the building energy consumption data shows a stable trend and the data correlation does not change significantly in multi-step prediction, the polynomial regression using this strategy can demonstrate the advantages of simplicity, intuitiveness, and strong interpretability and can predict the future energy consumption trend relatively accurately. However, when facing complex non-linear fluctuations or sudden change data, due to its limited ability to depict complex non-linear relationships, the prediction error may increase significantly.

When the SVR adopts the direct strategy, it uses an appropriate kernel function to map the input space to a high-dimensional feature space and constructs a regression model that can simultaneously predict the energy consumption for multiple future time steps. SVR has an excellent ability to handle non-linear relationships. When the building energy consumption data presents complex non-linear characteristics, this strategy can utilize its powerful non-linear mapping ability to predict the multi-step future energy consumption relatively accurately. However, this strategy is extremely sensitive to the selection of kernel functions and parameter adjustment. Improper settings are likely to cause the model to overfit or underfit, affecting the accuracy of multi-step prediction.

Under the direct strategy, the MLP constructs a multi-hidden-layer neural network, takes historical energy consumption and relevant influencing factor data as inputs, and directly outputs the energy consumption prediction values for multiple future time steps. MLP has a powerful non-linear function approximation ability. Theoretically, when facing complex building energy consumption data, it can learn complex patterns by adjusting the network structure and weights to achieve relatively accurate multi-step prediction. However, MLP is prone to falling into local optimal solutions and may encounter problems such as gradient disappearance or gradient explosion during training, which have a negative impact on its multi-step prediction performance under the direct strategy.

When the LSTM uses the direct strategy, it relies on its unique gating mechanism to effectively capture the long-term dependencies in time-series data. After inputting the historical energy consumption time-series data, LSTM can directly predict the energy consumption values for multiple future time steps. Given the significant time-series characteristics of building energy consumption data, the gating mechanism of LSTM enables it to retain historical information well in multi-step prediction and has certain advantages in predicting future energy consumption trends. However, the LSTM model has a complex structure, takes a long time to train, and requires a large amount of historical data. When the data volume is insufficient, it may affect the accuracy of multi-step prediction.

Based on the self-attention mechanism, the Transformer can efficiently capture the complex dependencies between vectors at any position in the input sequence. Under the direct strategy, the Transformer can simultaneously predict the building energy consumption for multiple future time steps. It has significant advantages in processing long-sequence data, can fully utilize the global information in historical energy consumption data, and can predict the multi-step future energy consumption relatively accurately. However, the Transformer model has a high computational complexity and requires a large amount of computational resources, which may be limited in practical applications.

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Recursive/rolling strategy

The recursive/rolling strategy is based on the prediction result of the previous step to make the next-step prediction. Taking the polynomial regression as an example, first, historical data are used to predict the energy consumption value of the first future time step, and then this predicted value is used as new historical data and combined with the original historical data to use the polynomial regression model to predict the energy consumption of the next time step, and so on. When the data are relatively stable and the noise is small, the polynomial regression using this strategy can gradually update the prediction and effectively track the energy consumption change trend to a certain extent. However, the prediction error of each step will continuously accumulate. As the number of prediction steps increases, the error may gradually increase, resulting in the prediction result deviating from the actual value.

Under the recursive/rolling strategy, SVR re-trains the model each time using the previous-step prediction result and historical data to predict the energy consumption of the next time step. Given SVR’s good ability to handle non-linear relationships, when facing building energy consumption data with certain non-linear change laws, this strategy can adapt to data changes by continuously updating the model. However, frequent model training will increase the computational cost, and when the previous-step prediction error is large, it will have a significant impact on subsequent predictions.

When MLP adopts the recursive/rolling strategy, it takes the previous-step prediction result as a new input feature and inputs it into the network together with other historical data to make the prediction for the next time step. The non-linear approximation ability of MLP helps to capture the dynamic changes in data during the recursive process. However, there is also the problem of error accumulation, and the network structure is complex. Each step of prediction requires re-calculating the network weights, resulting in a large amount of computation, which may affect the prediction efficiency.

Under the recursive/rolling strategy, LSTM can effectively integrate the previous-step prediction information into the next-step prediction by using the memory cell and gating mechanism. When dealing with building energy consumption time-series data with long-term dependencies, this strategy can continuously track the energy consumption change trend by continuously updating the memory cell. However, due to its complex model structure and large amount of computation, the error accumulation during the recursive process may also have a significant impact on the final prediction result.

Under the recursive/rolling strategy, Transformer focuses on the relationship between the previous-step prediction result and historical data through the self-attention mechanism and then makes the next-step prediction. The powerful global information capture ability of Transformer enables it to better use historical information to correct the prediction during the recursive process. However, due to its high computational complexity, the repeated calculations during the recursive process will greatly increase the computational cost, and the limitations of computational resources need to be carefully considered in practical applications.

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Sequential strategy

The sequential strategy is to train a separate model for each time step and then use these models in sequence according to the time order for prediction. In polynomial regression, polynomial regression models are constructed separately for different future time steps, and each model is trained only using the historical data before a specific time step. The advantage of this strategy is that each model is relatively simple, with a small amount of computation, and there is no problem of error accumulation. However, it is necessary to train a model for each time step, resulting in a large number of models, and the cost of model management and maintenance is high. Moreover, since each model is trained independently, it is impossible to fully utilize the correlation information between different time steps. For building energy consumption data with strong time-correlation, the prediction effect may be inferior to other strategies.

For SVR, an SVR model is trained separately for each future time step, and each model is trained and predicts based on the historical data and influencing factors before the corresponding time step. Since SVR can handle non-linear relationships, training a separate model for each time step can better adapt to the characteristics of data at different time steps. However, it also faces the problems of a large number of models and complex management, and there is a lack of information sharing among the models, which may lead to inconsistent prediction results.

Under the sequential strategy, MLP constructs an independent MLP network for each time step for training and prediction. Each MLP network learns based on the data before the corresponding time step and uses its non-linear approximation ability to predict the energy consumption of that time step. This method can optimize the model according to the data characteristics of each time step, but the increase in the number of models will bring problems such as long training time and high consumption of computational resources, and it is also difficult to fully utilize the overall information of time-series data.

Similarly, LSTM trains an independent LSTM model for each time step. Although LSTM can capture the long-term dependencies in time-series data, due to the independent training of each model, it is impossible to effectively utilize the coherence information between different time steps, and the ability to mine the complex time-series characteristics in building energy consumption data are relatively weak. Moreover, the training of a large number of independent models will consume a large amount of computational resources and time.

Under the sequential strategy, Transformer trains a Transformer model for each time step. The powerful self-attention mechanism of Transformer can independently capture the characteristics of the data before the corresponding time step in each model. However, similar to other models, under the sequential strategy, the number of Transformer models is large, the computational cost is extremely high, and there is a lack of collaboration among the models, making it difficult to fully utilize the advantages of Transformer in processing long-sequence data.

Different prediction models have their own advantages and disadvantages under the three multi-step prediction strategies of direct, recursive/rolling, and sequential. In practical applications, it is necessary to comprehensively consider factors such as the characteristics of building energy consumption data (such as data stability, non-linear degree, time-correlation, etc.), computational resources, and prediction accuracy requirements, and make a comprehensive trade-off to select an appropriate prediction model and multi-step prediction strategy to achieve accurate multi-step prediction of building energy consumption.

6. Conclusions

As society’s ongoing concern over the scarcity of natural resources and the degradation of the ecological environment intensifies, the construction industry is urgently seeking groundbreaking innovative technologies to accurately predict and optimize building energy consumption, thereby significantly enhancing energy efficiency. Among existing technological systems, building energy consumption forecasting emerges as an efficient strategy that is crucial not only for maintaining the stable operation of building energy systems but also for providing solid data support for managers to formulate scientific and rational operational strategies. This paper aims to comprehensively review the latest advancements in data-driven building energy consumption forecasting methods over recent years.

Based on different prediction time scales, building energy consumption prediction is segmented into very-short-term, short-term, medium-term, and long-term load forecasting. Each category carries specific research values and practical implications, which are thoroughly analyzed and explained with corresponding forecasting methodologies. Furthermore, for varying building types such as industrial, school, office, commercial, and residential buildings, their unique energy consumption characteristics are meticulously analyzed to uncover their inherent patterns. Additionally, from the perspective of energy types, this paper summarizes diverse energy forecasting strategies, offering insights for cross-domain applications.

A comprehensive framework for building energy consumption prediction techniques is constructed, with the forecasting process as the main thread. It elaborates on data acquisition strategies, the application of feature selection methods, the construction of data-driven prediction models, and comprehensive considerations for model evaluation and application practices. Multiple avenues for data acquisition are covered, including direct on-site collection, retrieval from public datasets, and software simulation generation, while also cautioning against potential data quality issues. In particular, seven key factors influencing building load, including meteorological information, indoor environmental information, occupancy data, temporal dimensions, building characteristics, socio-economic backgrounds, and historical energy consumption data, along with four efficient feature extraction techniques, are introduced in detail.

At the prediction model level, five mainstream data-driven models, including polynomial regression, SVR, MLP, LSTM, and Transformers, are analyzed in depth, revealing their unique strengths and applicable scenarios in handling complex energy consumption data. Meanwhile, by summarizing five core model evaluation metrics MAE, MAPE, MSE, RMSE, and R-squared, a quantitative basis is provided for objectively assessing model performance.

However, this study also has certain limitations. In terms of data, although multiple data acquisition methods are mentioned, direct on-site data collection often faces challenges such as insufficient data volume, high data collection costs, and data privacy protection. Moreover, public datasets may have problems such as inconsistent data formats and limited data coverage, which may affect the accuracy and generalization ability of model training. At the model level, the five mainstream data-driven models analyzed, although having advantages in their respective applicable scenarios, may experience a significant decline in prediction accuracy under special circumstances such as extreme weather conditions or major changes in building structures. Additionally, the training process of these models usually requires a large amount of computational resources and time costs. In terms of application scenarios, the current research mainly focuses on common building types. For some buildings with special functions or complex structures, such as large stadiums and hospitals, the relevant energy consumption prediction research is not in-depth enough, and there is a lack of targeted prediction methods and strategies.

Three cutting-edge research directions for data-driven building energy consumption prediction are envisioned: firstly, a multi-agent collaborative comprehensive energy forecasting and optimization system for more holistic energy management; secondly, a continuous optimization framework for deep learning models to enhance prediction accuracy and efficiency; and thirdly, the introduction of generative pre-trained models, which could revolutionize the field of building energy consumption forecasting.

It is believed that with the vigorous development of machine learning and deep learning technologies, data-driven technologies will continue to evolve, providing more efficient, reliable, and intelligent solutions for energy management and optimization in the construction industry, thereby propelling the industry towards a greener, lower-carbon, and more sustainable future.