Energy Consumption Calculation of Civil Buildings in Regional Integrated Energy Systems: A Review of Characteristics, Methods and Application Prospects

Abstract

Civil buildings play a critical role in urban energy consumption. The energy consumption of civil buildings significantly affects energy allocation and conservation management within regional integrated energy systems (RIESs). This paper first analyzes the influencing factors of civil building energy consumption, as well as the energy consumption characteristics of different types of buildings such as office buildings, shopping malls, hospitals, hotels, and residential buildings. Subsequently, it reviews methodologies for calculating operational energy consumption, offering valuable insights for the optimization and strategic adjustments of an RIES. Finally, the paper assesses the application potential of these calculation methods within an RIES and discusses the future development trend of calculating civil building energy consumption.

1. Introduction

With the continual progression of modern urbanization, notable phenomena such as population concentration and industrial restructuring have arisen in cities and their environs. One of the challenges posed by these transformations to urban development is energy provision [1,2]. Traditional energy supply systems, encompassing electricity, natural gas, thermal energy, and cooling energy, operate in isolation, thereby failing to exploit the energy synergy between systems effectively [3]. This leads to elevated energy operation losses, diminished energy utilization efficiency, impacts on the effective use and integration of renewable energy into the regional integrated energy system (RIES), and obstacles to achieving energy conservation and reducing pollutant emissions. Against the backdrop of markedly escalating energy demand and the pressing need for environmental preservation, in conjunction with the prevailing state of urbanization development, RIESs have emerged [4,5]. The RIES, situated on the demand side of energy, constitutes a comprehensive energy framework capable of catering to various energy demands of end-users within a specified area and integrating locally accessible renewable energy [6]. In the planning phase of an RIES, the calculation of energy consumption in civil buildings within the region holds pivotal importance [7]. This calculation not only impacts load prediction and energy management in regional buildings but also directly influences urban division, allocation of diverse energy types, energy transmission modalities, and system energy conservation management.

Civil buildings assume a role of vital importance in urban energy consumption, serving as not only the ultimate consumers of energy but also exerting a significant influence on the operation and optimization of RIESs [8]. The energy consumption characteristics of civil buildings are multifaceted and varied, encompassing heating, air conditioning, lighting, power equipment, and other facets, thereby underscoring the paramount importance of their energy consumption estimation. Against this backdrop, research on the calculation of operational energy consumption in civil buildings holds substantial significance [9]. Firstly, precise calculation of the energy consumption of civil buildings can provide vital insights for the planning and design of RIESs, thereby optimizing energy resource allocation and raising the energy utilization rate of the system [10]. Secondly, through the investigation of energy consumption in civil buildings, a scientific foundation can be laid for urban energy management and the formulation of strategies for energy conservation and emission reduction, thereby propelling the sustainable development of urban energy. Finally, research on the operational energy consumption of civil buildings can also offer a pragmatic foundation for the innovation and implementation of building energy conservation technologies, thus fostering the sustainable advancement of the construction industry.

This paper aims to scrutinize the calculation of operational energy consumption in civil buildings and delve into its significant importance for optimizing the planning and energy management of RIESs. Through the synthesis and analysis of extant research findings, alongside an exploration of future research trajectories, the objective is to provide theoretical support and practical experience for the advancement of RIESs and the efficient utilization of urban energy.

2. Energy Consumption Characteristics of Civil Buildings

In an RIES, identifying the operational building energy consumption is a crucial base for the allocation of energy. This paper mainly discusses the energy consumption associated with the operational phase of buildings. Gustavsson et al. [11] analyzed the primary energy use throughout the lifecycle of an eight-story wooden apartment. The results indicated that the operational energy consumption of building accounts for the highest category of energy consumption across all lifecycle stages and tends to gradually increase over time. Scheuer et al. [12] applied life cycle assessment (LCA) theory to calculate the energy consumption of a six-story building. The results indicated that the production, transportation, and construction of building materials account for 2.2% of the primary energy consumption in the life cycle, while the energy consumption of building operation accounts for 94.4%. Clearly, operational energy consumption represents a significant portion of a building’s lifecycle energy use, making it the most critical part of general building energy consumption. Therefore, reducing operational energy consumption is key to building energy conservation.

As a major component of energy consumption, the energy consumption patterns in the construction industry greatly affect the design and operational efficiency of the entire energy system. Therefore, deep insight into the operation energy consumption characteristics of different types of buildings and their influencing factors is important for an RIES to formulate effective energy strategies and optimize the energy system architecture [13]. This chapter mainly discusses the factors that influence energy consumption during the building operation phase from multiple dimensions and analyzes the energy consumption characteristics of office buildings, shopping malls, hospitals, hotels, and residential buildings.

2.1. Analysis of Influencing Factors of Building Energy Consumption

2.1.1. The Impact of Climate on Building Energy Consumption

The impact of climate on building energy consumption is significant because it is directly related to the demand for heating, cooling, and ventilation within a building. Climate change can significantly impact the energy needed for heating and cooling in buildings [14]. The research results of Frank [15] indicated that the annual cooling energy demand for office buildings with internal heat gains of 20–30 W/m² will increase by 223–1050%, while the heating energy demand will fall by 36–58%. Studies by Shibuya et al. [16] suggested that building energy consumption in Sapporo would remain almost unchanged in the event of future global warming. However, the energy consumption of buildings will increase significantly in Tokyo and Naha, where cooling energy consumption dominates. Their hot and humid environments require the long-term operation of air-conditioning systems to maintain indoor comfort, which significantly increases energy consumption. Invidiata and Ghisi [17] analyzed the impact of climate variations on building energy demand in three Brazilian cities. Their results indicate that by 2050, the energy needs in every year of the buildings in these cities are projected to increase by 56% to 112%. By 2080, this increase is expected to reach from 112% to 185%. Almasri et al. [18] conducted an analysis of the energy consumption patterns of residential buildings in the Kingdom of Saudi Arabia. Experimental data show that air conditioning accounts for a significant portion of building energy consumption, reaching as high as 67.34%. The energy consumption proportion of air conditioning is high due to the country’s hot climate. These studies have demonstrated that climate has a profound impact on the energy consumption of buildings. Climate is an indispensable factor in analyzing building energy consumption.

2.1.2. The Impact of Building Orientation on Energy Consumption

The orientation of the building determines the amount of solar radiation received on its surface. In the Northern Hemisphere, south-facing windows receive more solar radiation in winter, helping to naturally heat the interior space and thereby reducing heating demand. Conversely, additional shading measures may be needed in summer to avoid overheating. North-facing windows receive relatively less solar radiation, which can reduce the cooling load in summer. However, more heating energy consumption may be required in winter [19]. Windows facing east and west are exposed to sunlight at lower angles in the mornings and evenings, which can lead to additional energy for cooling in the summer. Particularly in summer afternoons, west-facing windows absorb substantial heat, potentially leading to increases in indoor temperatures and cooling energy consumptions. Laasri et al. [20] found that in the temperate regions of the Northern Hemisphere, reasonable building orientation can maximize the use of solar irradiance in winter for heating while reducing summer overheating through shading and ventilation. Large south-facing windows provide plenty of sunshine in winter, while in summer, excessive solar gain can be mitigated by external shading devices or properly designed eaves.

The orientation of the building also affects the efficiency of natural ventilation, which is essential to reduce air-conditioning usage and upgrade indoor air quality. In areas where the wind direction mainly occurs from a certain direction, the orientation and layout of the building can be designed to facilitate the flow of wind through the building and achieve effective ventilation. This not only reduces the energy needed for mechanical ventilation but also impacts the building’s heating and cooling demands [21].

2.1.3. The Impact of Building Envelope on Energy Consumption

The building envelope plays a critical role in influencing energy consumption of a building. The thermal conductivity of building envelope materials is a key indicator of their insulation performance. He et al. [22] analyzed the factors that influence the energy consumption of buildings in Jiangsu Province, China. The results showed that the thermal characteristics of the building envelopes, such as heat transfer coefficient, is a major factor affecting the energy consumption of buildings. Utilizing heat-barrier materials in the building envelope can delay the spread of heat, thereby reducing the building’s dependence on the heating, ventilation, and air-conditioning (HVAC) system and lowering the overall energy consumption of the building [23]. Song et al. [24] researched the impact of building materials on building energy consumption. The results indicated that incorporating phase change materials (PCMs) can enhance the heat absorption capacity of building envelopes and help reduce building energy consumption. In addition to the coefficient of heat conductivity of the material itself, the energy consumption of the building also varies with the thickness of the building’s external wall insulation layer. There is an optimal value for the thickness of the building’s external wall insulation layer, which minimizes annual energy usage [25].

In addition, the good air tightness of a building envelope is conducive to realizing building energy efficiency. The permeability of the material affects the natural ventilation capacity of the building, while their sealant properties influence air leakage rates. Good sealing performance reduces heat loss through gaps, especially in cold or extremely hot climates. Effective sealing can significantly reduce the load on HVAC systems [22].

Window design and materials also greatly impact building energy consumption. Modifying the thermal resistance of windows can change the loss and gain of heat, thus affecting the overall energy consumption. There are both single-layer windows and double-layer windows using air or argon as thermal insulation materials. Changing the thickness of window glass and glass gaps can change the heat transfer coefficient of the windows. According to the local climatic conditions, the best window configuration for building energy consumption can be found via adjusting [26].

2.1.4. The Impact of Occupant Behavior on Energy Consumption

Occupant behavior is a frequently overlooked factor in building energy consumption. Ultimate energy consumption depends on how individuals use various facilities and equipment, directly altering energy consumption patterns and amounts in the building. Occupants often interact with the building in various ways, resulting in huge deviation between actual and predicted building energy consumption. Esmaeil et al. [27] analyzed the energy consumption patterns of residential buildings in the Kingdom of Saudi Arabia. The results indicate that occupant behavior has a significant impact on the energy consumption of residential buildings. Ioannou et al. [28] found that when considering the impact of occupant behavior on thermostats and building ventilation systems, the impact of occupant behavior is more significant than the parameters of the building itself. Clevenger et al. [29] reported that occupant behavior can affect residential building energy consumption by up to 75% and office buildings by up to 150%.

Occupants’ habits of using HVAC systems, lighting systems, electrical appliances, and window management greatly influence overall building energy consumption. Hong et al. [30] studied 10 residential buildings of similar size and energy efficiency on the same street. Despite their similarities, the total energy consumption varied up to three times, primarily due to differences in HVAC usage. The main reason is the difference in the use of HVAC systems by residents. The maximum difference in energy consumption of HVAC systems between different residents was 5.2 times. Unnecessary lighting or the use of high-energy bulbs will increase energy consumption. The use of LED bulbs, use of automatic induction switches, and reasonable planning of indoor lighting can effectively reduce energy consumption, which is particularly evident in buildings with high lighting energy consumption such as office buildings. Yun et al. [31] studied the effect of occupant behavior on office lighting energy consumption and found that changes in the use of office building lighting systems could lead to a 50% difference in lighting energy consumption.

Ahmadi-Karvigh et al. [32] conducted a study on the impact of occupant behavior on the energy consumption of electrical appliances. The subjects of the study included different types of buildings such as offices and apartments. The results showed that there is a great difference in the use habits of electrical appliances. On average, 35.5% of the energy consumption of electrical appliances is considered to be potentially reducible. Window operation habits will affect the thermal insulation effect and air-conditioning efficiency of the building. In winter, improper window opening will increase heating demand, while in summer, it may lead to the loss of cooling air and increase the burden of air conditioning. Wang et al. [33] analyzed the impact of different occupants’ use of windows on building energy consumption. Under different climatic conditions, their proposed window operation mode can save 17–47% of HVAC energy consumption. Sun et al. defined five occupant behaviors. They analyzed the impact of these five occupant behaviors on building energy consumption and found that the comprehensive implementation of the five occupant behaviors can achieve a 41% increase in energy saving.

2.1.5. The Impact of Thermal Comfort on Energy Consumption

When researching building energy consumption, we must consider the impact of the thermal comfort of personnel inside the building on energy consumption. Some of the important objectives of architecture for humanity is to improve the living environment and enhance safety and comfort. With the development of society and technological progress, people are increasingly pursuing comfort in the interior environment of buildings, and building energy consumption is also increasing.

The thermal comfort prediction model plays an important role in predicting building energy consumption. By using an adaptive thermal comfort model and the predicted mean vote (PMV), thermal comfort analysis was conducted on different types of residential buildings; Aiman Albatayneh et al. [32] indicated the impact of the thermal comfort of occupants on building energy consumption. Based on research on personnel thermal comfort, the impact on building energy consumption was analyzed. Yang L. et al. [33] compared buildings with natural ventilation; the PMV model yielded more accurate results in buildings with air conditioning. After using an adaptive model for thermal comfort analysis, it was found that it has a larger comfortable temperature range.

In summary, the thermal comfort of personnel will have an impact on the calculation and prediction of building energy consumption.

2.2. Energy Consumption Characteristics of Different Types of Buildings

The RIES may involve a variety of different types of buildings. The energy consumption characteristics of different building types varies, and they should be analyzed separately.

2.2.1. End-Use Energy Characteristics of Office Buildings

Windows make a building more beautiful and provide a good view, so they have widely been used in office buildings [34]. Common office buildings usually adopt a glass curtain wall structure with a high window-to-wall ratio (WWR). A higher WWR leads to increased energy consumption, primarily because the thermal conductivity of the glass increases the cooling load requirements during the summer [35]. In addition, such buildings generally have good sealing performance and rarely use natural ventilation. In order to ensure indoor air quality, ventilation systems have to be used to complete air exchange, which brings additional energy consumption. Modern office buildings are equipped with modern networks, security systems, office automation, and building automation systems due to functional requirements. These not only increase the power load of office buildings but also greatly increase indoor heat generation and further aggravate end-use energy consumption [36].

The end-use energy consumption characteristics of office buildings include the following:

• Predictability over time: Generally, the use of office buildings follows a clear time pattern. Most of the energy consumption is generated during the day, and the energy consumption level of office buildings at night is very low.
• High demands for cooling and heating: Office buildings have high requirements for indoor environmental comfort, necessitating cooling in summer and heating in winter. Especially for high-end office buildings, this requirement is higher [37]. In addition, the use of multitudinous office equipment increases the release of indoor thermal energy and brings more summer cooling load.
• Significant energy consumption by office equipment: So as to better assist personnel, modern office buildings are equipped with a large number of office automation equipment such as computers, printers, and projectors. These devices bring great convenience to users but also increase power burden [38].
• High lighting energy demand: Due to the high requirements of office buildings for lighting, indoor ordinary lighting and decorative lighting are also important parts of energy consumption [39].
• High reliability requirements for energy supply: The daily work of office personnel is highly dependent on automation equipment. Insufficient energy supply may have a serious impact on the office and cause various economic losses. Therefore, for office buildings, insufficient energy supply is unacceptable.

2.2.2. End-Use Energy Characteristics of Shopping Mall Buildings

Shopping mall buildings are characterized by high foot traffic, extensive use of air-conditioning systems, and long operating hours, making their end-use energy consumption per unit area higher compared to other types of public buildings [40].

Shopping mall buildings generally have the following characteristics in terms of end-use energy consumption:

• High electricity consumption for lighting: The artificial lighting area inside the mall is large, so there is generally no dark area, and some shops also need independent lighting. To enhance the shopping environment and attract customers, shopping malls often use a variety of high-intensity lighting fixtures to emphasize quality and class [41].
• Significant proportion of cooling in total energy use: The air-conditioning and heating system of the mall needs to cover a large area of changeable space, including the main shopping area, warehouse, employee area, and so on. In addition, the indoor personnel are densely packed, and a large amount of heat is generated. This requires an efficient central air-conditioning system [42].
• Seasonal variation in energy consumption: During summer, the demand for cooling is higher, leading to increased total energy consumption in the building. In winter, the higher density of occupants inside the shopping mall generates more indoor heat, reducing the need for heating. Consequently, this results in the lower overall energy consumption of buildings.

2.2.3. End-Use Energy Characteristics of Hospital Buildings

The main components of end-use energy consumption in hospital buildings include air conditioning, medical equipment, lighting, domestic hot water, office equipment, heating, etc. [43]. The structure of end-use energy consumption in hospital buildings is complex; the energy forms are diverse. The end-use energy consumption of hospital buildings is inherently high due to the need to ensure uninterrupted service throughout the year [44].

The end-use energy consumption characteristics of hospital buildings are generally as follows:

• Strong seasonality with the highest energy consumption in summer: The end-use energy consumption of hospital buildings is primarily concentrated during the summer months (June-September), accounting for almost half of the annual end-use energy consumption. Even in early and late summer, energy consumption is higher than in other periods.
• High energy consumption in winter for heating: So as to provide a good convalescent environment for patients and meet the operational requirements of some equipment, the hospital needs to strictly control the room temperature and humidity. The continuous need for hot water leads to high energy consumption, which is on a rising trend [45].
• High energy consumption by ventilation systems: In order to prevent the spread of diseases in the ventilation system, hospitals need high-efficiency particulate air (HEPA) leaches to filtrate air. Areas such as operating rooms and intensive care units have higher requirements for indoor air quality. These rooms need to be ventilated 20–30 times per hour, so the energy consumption level of the hospital ventilation system is notably high [46].

2.2.4. End-Use Energy Characteristics of Hotel Buildings

Hotel buildings generally have a large building area and tall structures, but the end-use energy consumption structure is relatively simple, mainly concentrated in air conditioning, hot water, lighting, and others [47].

Compared with other types of public buildings, the end-use energy consumption of hotel buildings generally has the following characteristics:

• Significant daily fluctuations in overall end-use energy consumption: Various facilities such as restaurants, leisure centers, business centers, and laundry rooms in the hotel have different operating hours due to their different functions and service objectives. For example, restaurants may operate at high loads during breakfast, lunch, and dinner and are relatively idle at other times. Leisure centers such as gyms or swimming pools are frequently used in the morning and evening peak periods, and other periods are relatively cold. The intermittent operation caused by this function makes the energy consumption fluctuate significantly in different time periods.
• High uncertainty in end-use energy consumption: Hotel occupancy rates vary with time, such as higher occupancy rates during holidays. The fluctuation in occupancy rates during different periods results in significant variations in end-use energy consumption. However, in most cases, hotels operate under partial loads. Hotel energy consumption is closely related to occupancy rate. During holidays or special events, high occupancy rates often lead to a raise in overall energy consumption, especially in air conditioning, hot water supply, and elevator use. In the off-season or off-peak hours, even if the hotel maintains basic operations, end-use energy consumption will be reduced due to low-load operation [48].
• Significant impact of guest behavior on end-use energy consumption: Usually, the parameters of the equipment in the hotel room can be adjusted by the customer, and the energy consumption will be different. The hotel room provides a high degree of autonomy to the guests [49]. This kind of self-regulation results in energy consumption uncertainty, because different guests’ usage habits and preferences may lead to a large difference in energy consumption of the same room at different times [50].

2.2.5. End-Use Energy Characteristics of Residential Buildings

Residential buildings include all standalone houses and apartments, and the energy consumption of residential buildings in the United States is shown in this study. Due to the large differences in the parameters of different residential buildings, the energy consumption patterns of residential buildings are significantly affected by the type of building [51]. Due to the external walls all around, standalone houses usually exchange more heat with the outside world and are more dependent on HVAC systems, so their energy consumption is relatively high in winter and summer. By contrast, the end-use energy consumption of apartment buildings depends more on the efficiency of shared walls and central heating and cooling systems [52]. In addition, the size of the residential area, layout, building materials, and other factors will have a significant impact on their end-use energy consumption.

Residential end-use energy consumption is significantly affected by residents’ daily living habits [53]. For example, frequent use of hot water, high-energy appliances, and unreasonable indoor temperature settings can increase energy consumption. Adjusting the thermostat to reduce heating or increase cooling when family members are not at home can significantly reduce energy use. In addition, the selection of energy-efficient lamps and appliances, as well as the appropriate use of curtains and blinds to adjust natural light, can also effectively reduce energy consumption. Therefore, raising residents’ awareness of energy consumption and encouraging more energy-efficient living habits are key to reducing residential energy consumption.

2.3. Brief Summary

In this chapter, we have conducted an analysis of the energy consumption characteristics of different building patterns and their influencing factors. In traditional energy management, building energy consumption pays more attention to a single building, focusing on the energy efficiency improvement of the building itself and the implementation of energy-saving measures. Its scale and scope of influence are relatively small. The RIES emphasizes collaborative management across multiple energy and building types, which further enhances the necessity of accurate energy consumption calculation.

In an RIES, the energy demand and supply of building groups containing different types of buildings in a region needs to be considered, and overall energy planning and management needs to be carried out. This means that there is occasion to consider the energy consumption characteristics, energy sources, energy conversion and energy reserves of different types of buildings in order to realize efficient energy consumption and balance supply and demand in a region. Accurate energy consumption calculation can enable energy managers to implement more effective energy allocation and demand response strategies, thereby reducing costs, increasing the reliability of energy supply, and supporting sustainable development goals. Therefore, accurate calculation of building energy consumption not only helps to optimize the performance of a single building but also helps to improve the operational efficiency of the entire RIES.

Through this comprehensive analysis, this chapter aims to help readers deeply understand the energy consumption characteristics and influencing factors of building types that may be included in an RIES. Understanding the characteristics of building energy consumption in an RIES is the basis for designing more efficient and sustainable energy strategies for it. It is also a key to promoting RIES innovation and improvement.

3. Methods for Calculating Operational Energy Consumption of Civil Buildings

There are two prevalent approaches for assessing energy consumption: top–down calculation methods and down–top calculation methods. The top-down methods involve calculating energy consumption by obtaining the overall energy consumption within a certain area or system and dividing it down into a single building, single equipment, or single behavior based on the energy consumption of different categories within the area or system. On the other hand, the down–top approach obtains the overall energy consumption of a certain area or system by obtaining the energy consumption generated by each room, device, or behavior and conducting statistics according to different categories. Such studies yield valuable insights and references for building facilities, construction practices, and operations.

3.1. Top–Down Methods for Calculation

In the context of regional integrated energy systems, top–down energy consumption estimation methods for residential buildings typically begin by analyzing the total energy consumption of the region. All residential buildings within the region are collectively considered as a single energy-consuming entity. The impact of these buildings on energy consumption is assessed based on long-term trends or transitions observed in the building sector and the economy–energy relationship. This assessment is crucial for determining the region’s energy supply requirements [54]. The optimal approach for obtaining comprehensive data on total building energy consumption within a region is through the efforts of statistical departments employing survey methodologies. In many International Energy Agency (IEA) member countries, including the United States, Japan, South Korea, and the United Kingdom, detailed information regarding energy consumption within the building sector is readily available in energy statistical yearbooks or data handbooks [55].

In addition to total building energy consumption statistics, these countries conduct specialized surveys on building energy consumption to obtain detailed insights into energy usage patterns across different climate zones, building types, and functional categories, thereby supporting energy-saving initiatives. In the United States, separate building energy consumption surveys are conducted for the commercial and residential sectors, each carried out every four years. The commercial building survey covers approximately 6720 buildings, while the residential survey involves about 5600 households. The findings from these surveys are published by the U.S. Energy Information Administration (EIA) [56]. In Japan, the Institute of Energy Economics (IEE) conducts comprehensive research and analysis to systematically gather energy consumption data for both residential and commercial sectors. These data are annually published in the Energy Economy Statistical Handbook [57]. In South Korea, guided by the Energy Rational Use Act, a comprehensive survey of terminal energy consumption, known as energy consumption statistics research, has been conducted every three years since 1981. This survey provides critical insights into energy consumption patterns and trends, facilitating informed energy policy decisions and the implementation of energy efficiency measures [58].

The Ministry of Housing and Urban-Rural Development of China launched initiatives to systematically monitor civil building energy consumption with the introduction of the Civil Building Energy Consumption Statistical Report System (trial) in 2007, aimed at the specialized statistical analysis of building energy usage [59]. By 2009, this survey had expanded nationwide, and during this expansion phase, the statistical policies underwent six revisions [60]. After determining the total energy consumption of civil buildings within a region, specific types of energy consumption for individual buildings are typically calculated based on the building types within that region. This process can then be further broken down to specific equipment-level energy consumption. In China’s “Civil Building Energy Resource Consumption Statistical Report System” [61], urban areas are classified into four main categories for statistical purposes: national government office buildings, large public buildings, medium and small public buildings, and residential buildings (including mixed-use commercial and residential buildings). The statistical content includes fundamental building information, energy consumption from sources like electricity, coal, and gas, and information on centralized heating systems and building energy conservation measures.

This policy utilizes a sampling survey method to systematically and comprehensively gather data on heterogeneous patterns of buildings, including public buildings, residential buildings, rural housing, and northern heating systems, across 106 key cities throughout China. It is considered the largest-scale building energy consumption statistics project globally. The majority of countries adopt the IEA’s energy balance statistical model, which broadly categorizes final energy consumption sectors into industry, buildings, and transportation categories. Within the building sector, there is a specific division between commercial and residential segments. The breakdown of final energy consumption sectors in several key IEA member countries is detailed in

Table 1. IEA final energy consumption sector division [62].

Country	Final Energy Consumption Sector Division
USA	Industry, Transportation, Residents, Commerce
Japan	Industry (Agriculture, Manufacturing, Construction), People’s wellbeing (Family, Business), Traffic (For customer use, Freight use), Non energy consumption
Korea	Industry (Agriculture, Manufacturing, Construction, Mining), Traffic (Service, Private), Residence, Trade, Public, and Others
England	Industry, Transportation, Others (Family, Public, Commerce)

.

The distinct categorization of the building sector in energy consumption statistics leads to the availability of building industry energy consumption data in resources such as energy consumption yearbooks, manuals, reports, and similar publications. However, in certain countries like China, energy consumption statistics are closely linked to economic activities. In China’s context, energy consumption is classified into four primary sectors based on production methods: primary industry, secondary industry, tertiary industry, and the household sector. Final energy consumption statistics primarily focus on types and quantities without specific usage distinctions [63]. While this statistical approach is effective for aggregating overall energy consumption across regions, the inability to directly access building-related energy consumption data may result in some data redundancy. Moreover, with the advancement of regional integrated energy systems, enhanced precision in energy transmission and distribution is imperative. Therefore, there is a necessity to disaggregate energy consumption data as much as possible during statistical analysis.

Particularly for public and residential buildings, variations in energy usage patterns during peak and off-peak periods, differences in energy consumption activities, and varying types and quantities of consumed energy all impact the energy supply and distribution within regional integrated energy systems. In addition to employing a top–down approach, the total operational energy consumption of civil buildings within a region can also be determined using a bottom–up method. This approach starts with individual buildings, establishing the energy consumption of each unit, and then aggregates these data based on sectors and industries to derive the overall operational energy consumption of civil buildings within the region.

In China, due to current energy consumption rates, statistical methods do not directly provide building-related data but disperse them across three major industries and four major sectors. Therefore, in China, various methodologies exist to assess the energy consumption of civil buildings within a region, including those analyses rooted in statistical yearbooks, energy balance sheets, end-user energy consumption data, and field surveys.

3.1.1. Methods Based on Statistical Yearbooks for Calculation

In most countries participating in the IEA, data on building energy consumption are typically sourced from energy consumption yearbooks, manuals, reports, or similar publications to access national or regional totals. Operational building energy consumption data can then be acquired directly from these resources or by extracting proportional data from energy consumption yearbooks or related documents. Allouhi et al. [54] provided a comprehensive overview of energy consumption data for residential and commercial buildings across various countries based on energy consumption yearbooks or similar publications. They also conducted energy disaggregation for specific countries and identified key influencing parameters. Reuter et al. [64] analyzed the relationship between regional energy policies and building energy consumption using variations in energy consumption data obtained from statistical yearbooks.

In China, due to differences in energy statistical methods compared to other countries, building operational energy consumption is calculated based on corresponding energy consumption statistics for the three major industries and four major sectors. Residential building operational energy consumption primarily comprises electricity for household use and consumption of various fossil fuels. These data can be obtained from relevant energy consumption statistics within the residential sector of statistical yearbooks. Public building energy consumption mainly originates from sectors within the tertiary industry, encompassing commerce, accommodation and catering, finance, real estate, business, public services, and management organizations. Electricity consumption for these sectors is documented in statistical yearbooks. Additionally, public buildings typically utilize energy sources other than electricity during operation. The statistical values for these other energy sources are derived from sample surveys regarding the proportional energy consumption of various public building types, calculated based on electricity consumption figures [65].

Plenty of experts are interested in residential building energy consumption based on statistical yearbooks. For example, Li et al. combined data from the Guangzhou Statistical Yearbook to analyze the total energy consumption and energy intensity of buildings in Guangzhou [66]. Qiao et al. [67] proposed a consistent statistical approach and calculation method based on statistical yearbook data to analyze building energy consumption intensity in Sichuan Province. Liu et al. [68] initially studied energy consumption data from various statistical yearbooks in China and then calculated building energy consumption based on statistics from the Shandong Statistical Yearbook. Ma et al. [69] proposed a method for calculating terminal energy consumption in residential and public buildings in Shenzhen based on statistical yearbook data. They calculated the terminal energy consumption of residential buildings in Shenzhen by establishing a model for terminal energy consumption in civil buildings.

In the planning of regional integrated energy systems, the consumption of various energy types serves as a critical design parameter. The building operational energy consumption calculation method based on statistical yearbooks allows for data acquisition ranging from the total energy consumption of the entire regional integrated energy system to the specific energy consumption of individual units. This method is crucial for assessing building operational energy consumption within regional integrated energy systems and provides vital data support for designing energy allocation across various units within the system. However, given potential issues such as data gaps or duplication in energy consumption yearbooks and potential discrepancies in understanding statistical criteria and calculation rules across reporting levels, it is essential to carefully utilize and review energy consumption data when calculating building operational energy consumption.

3.1.2. Methods Based on Energy Balance Sheets for Calculation

Energy balance sheets provide a comprehensive overview of the supply, consumption, and transformation of different energy categories within a country or region over a specific period, illustrating the equilibrium among energy supply, processing, conversion, and final consumption. However, in China’s energy balance sheet, final energy consumption sectors are classified into seven categories: (1) agriculture, forestry, animal husbandry, and fisheries; (2) industry; (3) construction; (4) transportation, warehousing, and postal services; (5) wholesale, retail, accommodation, and catering services; (6) other sectors; and (7) residential life. Notably, building energy consumption is not individually listed and must be derived by aggregating data from various industries [70]. The methodology for calculating building energy consumption in each province is outlined based on the disaggregation of the energy balance sheet, as illustrated in Figure 1. Utilizing statistical data from the energy balance sheet, energy consumption for different building types is computed at the national level, offering comprehensive and comparable insights [71]. This process establishes a robust data foundation for statistical analysis of building operational energy consumption, emphasizing the importance of excluding non-building energy consumption from the calculations.

Wang et al. [72] compared China’s energy balance sheet with the general guidelines released by international energy statistical agencies, calculating the energy consumption of various sectors and categories in China; then, they clarified the energy consumption scope within each classification of China’s energy balance sheet, provided the latest energy efficiency data, and conducted an analysis of influencing factors, focusing on elucidating the differences in energy consumption data between the electricity consumption method and the heat method within the energy balance sheet. Cai et al. [70] constructed a model for disaggregating energy consumption in civil buildings based on the statistical approach of the energy balance sheet. They proposed a method for checking the upper and lower limits of regional building energy consumption and completed the disaggregation process.

Wang et al. [73] corrected the building energy consumption data in the energy balance sheet using the building energy consumption calculation methods from the China Building Energy Efficiency Committee and analyzed the development trends of building energy consumption in China. Yin et al. [74] analyzed and studied the current status of terminal energy consumption obtained from the new normal of the economy and based on data from the energy balance sheet. They calculated the terminal energy consumption of buildings and conducted a detailed analysis of the development characteristics of building terminal energy consumption. Deng et al. [75] calculated the building energy consumption in Chongqing from 2001 to 2014 based on the energy balance sheet. They analyzed the increasing trend of building operational energy consumption in Chongqing using time-series data, noting that it accounted for 10–19% of total social energy consumption. Jing et al. [76] employed a top–down approach to estimate building energy consumption within urban areas using data from provincial-level energy balance sheets, with an error range within 10%.

Regional disparities among various areas are substantial, and prior research has conducted comparative analyses using different economic zoning, climate zoning [77,78], and geographical environmental zoning [79]. Liu et al. [80], employing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, performed an analysis and pinpointed key driving factors influencing building operational energy consumption, such as total population, residential consumption levels, tertiary industry development, per capita floor area of buildings, and urbanization rate. Furthermore, distinct lifestyles can also exert significant influence on building operational energy consumption [81].

The approach to calculating the operational energy consumption of civil buildings based on the energy balance sheet is effective for quantifying energy use resulting from various production and living activities, and it allows for the evaluation of different types of operational energy consumption across different building categories. This method is beneficial for informing the planning of regional integrated energy systems. However, inherent shortcomings in energy statistics akin to those in energy statistical yearbooks can lead to data gaps or duplicate entries within the energy balance sheet. Additionally, notable discrepancies in energy consumption data arising from different calculation methods pose challenges for ensuring horizontal comparability of building energy consumption across diverse regions, highlighting the need for meticulous verification during calculations.

3.1.3. Methods Based on End-Use Energy Consumption

Terminal energy consumption refers to the amount of energy utilized within a regional integrated energy system after accounting for losses during transmission from primary energy sources. This methodology involves categorizing and consolidating the operational energy consumption of buildings based on building types and human energy demands; subsequently, an established energy consumption model is used to calculate and aggregate building energy usage, and ultimately, authoritative data on different building types are employed to determine regional operational energy consumption for buildings.

Tsinghua University’s Building Energy Research Center developed the China Building Energy Model (CBEM), which uses a bottom–up approach. This model, based on extensive statistical surveys and measurements, established a database of typical buildings in China. It captures comprehensive building performance under varying meteorological conditions, building structures, equipment performance, and behavioral patterns [82], as illustrated in

Table 2. China Building Energy Model.

	First Level Sub Item	Secondary Sub Items
China Building Energy Model	Building type and climate zoning	Public buildings
		Urban residential buildings
		Rural residential buildings
		Severe cold zone
		Cold zone
		Hot summer and cold winter zone
		Hot summer and warm winter zone
		Moderate zone
	Northern urban heating (NUH)	Cogeneration, combined heat and power (CHP) systems
		Coal boilers
		Gas boilers
		Industrial excess heat
		Coal stoves
		Household gas boilers
		Heat pumps
		Electric heaters and others
	Public buildings (excluding NUH)	Government offices
		Commercial offices
		Lodging buildings
		Retail buildings
		Education buildings
		Health care buildings
		Others
	Urban residential buildings (excluding NUH)	Space heating in hot summer and cold winter zone
		Space cooling
		Lighting
		Water heating
		Cooking
		Appliances
	Rural residential buildings (excluding NUH)	Space heating in the north/south
		Space cooling
		Lighting
		Cooking and water heating
		Appliances

.

Building upon this foundation, Tengfei Huo et al. [83] proposed the China Building Energy Consumption Model (CBECM), which disaggregates data from statistical yearbooks and energy balance sheets. Compared to the CBEM approach, CBECM is more concise and relies on more authoritative data. Additionally, Urban Building Energy Modeling (UBEM) employs a bottom–up physical method to simulate building energy consumption at regional or city scales [84]. This model is refined by incorporating resident behavior [85], model characteristics [86], and other factors to enhance realism and applicability. Additionally, in empirical research, Yue et al. [87] introduced a novel method for monitoring building energy consumption. This approach involves energy identification within residential buildings, enabling the real-time monitoring of terminal energy consumption and proposing strategies for energy consumption control in residential settings.

The terminal energy consumption calculation method employs a bottom–up approach to assess the operational energy consumption of buildings within a region. This method is more precise compared to earlier approaches but depends on the accuracy of the established terminal energy consumption calculation model. As societal activities evolve over time, changes in production and living patterns can influence the energy consumption structure across different building types. Consequently, existing terminal energy consumption calculation models may exhibit significant deviations, necessitating continuous updates based on actual production and living activities. This calculation method aligns well with regional integrated energy system planning by enabling separate calculations of operational energy consumption on the supply side, transmission segment, and demand side. However, it lacks robust data support for more detailed levels of energy disaggregation.

3.2. Down–Top Methods for Calculation

Down–top studies focus on individual buildings or groups of buildings, analyzing various factors that influence building operational energy consumption. By further disaggregating building operational energy consumption into several units based on time, equipment, human behavior, and other factors, researchers can calculate operational energy consumption within a region. This approach enhances the accuracy of predicting operational energy consumption for different parts of the region, which is valuable for regional integrated energy system planning. By forecasting operational energy consumption across different time periods, energy categories, and building types, in conjunction with regional development trends, it is possible to extend the efficient operation of regional integrated energy systems.

Specifically, this involves three main aspects: the design of sub-metering systems, analysis of energy consumption influencing factors, and analysis of electricity usage behavior. The design of sub-metering systems ensures detailed monitoring of various energy-consuming components within buildings and serves as the foundation for energy consumption assessment. The analysis of energy consumption-influencing factors helps uncover the key factors and mechanisms contributing to building energy consumption, while the analysis of electricity usage behavior highlights the significant impact of user behavior on energy consumption. Integrating these three aspects enables a comprehensive and in-depth understanding of the down–top methods of residential building energy consumption.

By starting with sub-metering, energy consumption-influencing factors, and electricity usage behavior, researchers can reveal the main characteristics and influencing factors of energy consumption at each unit that generates energy consumption. This combined approach provides practical methods and strategies for energy consumption assessment, supporting effective energy management and decision making for energy savings.

3.2.1. Methods Based on Sub-Metering System Design for Public Buildings

Public buildings are characterized by their high energy density and centralized management. The establishment of a sub-metering system is recommended to streamline management processes. In contrast to traditional metering methods, a sub-metering system offers a comprehensive perspective on building energy consumption [88]. It enables a detailed analysis and evaluation of various factors influencing energy usage, including building characteristics, external environment conditions, and equipment utilization. This system facilitates meticulous scrutiny of energy consumption patterns, accurately identifying peak consumption periods and high-energy-consuming equipment. Consequently, it provides specific directions and measures for optimizing energy consumption.

Table 3. China Building Energy Model.

	First Level Sub Item	Secondary Sub Items
Total electricity consumption of buildings	Electricity consumption for lighting sockets	Lighting and socket electricity consumption
		Electricity for corridors and emergency lighting
		Outdoor landscape lighting electricity consumption
	Electricity consumption for air conditioning	Electricity consumption for cold and hot stations
		Electricity consumption at the end of air conditioning
	Power consumption	Elevator electricity consumption
		Water pump electricity
		Ventilation fan electricity consumption
	Special electricity usage	Information Center, Laundry room, Kitchen, Restaurant, swimming pool, Gyms and other special electricity uses

illustrates a classification model derived from the guidelines of the Chinese Ministry of Housing and Urban-Rural Development.

Establishing a sub-metering system relies heavily on energy consumption classification models, which serve as essential sub-categories. Following the guidelines outlined by the Ministry of Housing and Urban-Rural Development, Feng [89] conducted a further disaggregation of energy consumption in a public building located in Shanghai. Utilizing methods such as on-site visits, inquiries, and data analysis, Meng proposed energy-saving recommendations for the building. Xue [90] and colleagues introduced energy consumption bill disaggregation, employing both temporal methods and the DeST energy consumption simulation method to calculate the energy consumption of various sub-items within hospital buildings. They also validated the effectiveness of energy consumption analysis methods through case studies. Yang et al. [91] further expanded on the energy consumption classification model of public buildings by subdividing electricity consumption indicators into 27 sub-indicators. They utilized indirect measurement methods to measure and statistically analyze each classified energy consumption, deriving an optimal disaggregation algorithm for energy consumption and establishing a mathematical model for the rapid calculation of building energy consumption. Li et al. [92], utilizing sub-metering statistical methods, applied time series forecasting techniques to precisely analyze the energy consumption of each sub-item, thereby improving the energy utilization efficiency of buildings and fully exploiting their energy-saving potential. Jialin Wu et al. [41] proposed a regression model based on sub-metering systems, assessing building energy consumption levels through sub-item energy consumption indices and offering a pathway for online energy-saving diagnosis in large public buildings.

The establishment of sub-metering systems and the introduction of indirect measurement methods allow for the systematic acquisition of continuous operational data for various categories within the system, providing data support for refining building operational energy consumption and planning comprehensive regional energy systems.

3.2.2. Methods Based on Analyzing Influencing Factors of Operational Energy Consumption in Buildings

The analysis of factors influencing building operational energy consumption entails a thorough examination and assessment of various elements impacting energy usage, encompassing building characteristics, external environment, and equipment utilization. This analysis provides insight into the underlying mechanisms driving building energy consumption, offering theoretical support for accurate energy consumption estimation [93]. Furthermore, quantitative analysis and forecasting, derived from the analysis outcomes, unveil the specific contributions and levels of influence that different factors exert on energy consumption.

Pei et al. [94] proposed a method using the vector autoregressive model (VAR) to select macro-level factors influencing energy consumption in civil buildings. They identified three macro indicators affecting building energy usage: total population, added value of the tertiary industry, and energy intensity. The ADF unit root test was utilized to assess the variables’ stationarity, and building energy consumption was calculated based on these influencing indicators. Payam et al. [95] observed in their study that population, urbanization, and economic growth are pivotal factors contributing to the rapid surge in energy consumption in developing nations.

Rinaldi et al. [96], Delzendeh et al. [97], and other researchers examined the impact of activity behaviors among different population groups on building operational energy consumption, drawing insights from questionnaire survey findings. M. Bourdeau et al. [98] provided a comprehensive overview of eight data-driven models for building energy consumption, considering factors such as building type, usage, energy requirements, and objectives. They emphasized the pivotal role of human behavior in data-driven modeling, incorporating cutting-edge technologies and research findings. T. Sekki et al. [99], in their study focusing on school buildings in southern Finland, investigated overall energy consumption patterns and assessed factors influencing building energy usage. Their findings revealed that buildings with shorter operating hours tended to consume less energy, with variations observed across different types of educational buildings. Ma et al. [100], drawing from data collected from 40 public buildings in Tianjin, discovered that the functionality of building envelope structures and lighting systems had a significant impact on building energy consumption. Maryam et al. concentrated their research on mosques. Acknowledging the distinct operating hours and functionalities of these structures, they introduced a methodology for estimating building operational energy consumption using a deep learning model. This approach involved the selection of factors like building operating schedules and light intensity as influential indicators to forecast building operational energy usage [101]. Ma et al. [102] delved into the correlation between building energy consumption indicators and data. Employing an object-oriented methodology, they crafted a comprehensive model of building information, designing granularity models for three interrelated entities. These entities collectively constituted the ultimate information model of the building, which was leveraged for extensive big data analysis. Subsequently, they utilized data software to construct a database of building energy consumption information grounded in the model and conducted a validation analysis to ascertain feasibility. Wang et al. [103] conducted an analysis by selecting four influencing factors at three levels that affect building energy consumption. Utilizing orthogonal experimental design, they examined the primary and secondary relationships among these factors regarding the building’s annual energy consumption. Through variance analysis, they determined the significance levels of each factor on building energy consumption. Ultimately, they derived an optimal combination of four factors for when the building’s annual cooling load is minimized. This research provides insights and theoretical foundations for energy-efficient building design.

Combining the analysis of factors influencing buildings’ operational energy consumption can provide support for establishing relevant models to measure and forecast a building’s operational energy consumption, as well as offer references for the planning of regional integrated energy systems.

3.2.3. Methods Based on Analyzing Energy Use Behavior in Residential Buildings

The advancement of metering technology has led to the accumulation of extensive data within metering and statistical systems, where energy consumption patterns and specific information are often embedded within smart meters, gas meters, and similar devices reflecting user consumption behavior [104,105].

Energy behavior analysis involves examining the energy consumption habits and behavioral tendencies of residents or users within buildings, along with assessing the impact of these behaviors on building energy consumption. Through such analysis, personalized identification and evaluation of individual user energy consumption can be achieved, offering a more nuanced foundation for precise energy consumption estimation [106]. By integrating the outcomes of energy behavior analysis, it becomes feasible to guide users to alter their poor energy consumption habits, thereby mitigating overall energy consumption within buildings.

Hamed et al. [107] correlated the calculation of energy consumption for heating, air conditioning, and other subsystems with the habits of building occupants, analyzing how user energy behavior influences the energy demand of buildings, thereby emphasizing the intimate connection between energy behavior and building energy consumption estimation. Heinrich et al. [108] integrated user energy consumption patterns with their lifestyles, conducting cluster analysis on 35 influencing factors to develop an energy consumption analysis model based on household behavior and energy usage patterns. Their findings indicated a correlation between residential energy behavior, lifestyles, and building energy consumption levels. Douglas et al. [109] evaluated the interaction between user energy behavior and subsystems such as air conditioning, lighting, and ventilation, quantifying the impact of user behavior on building energy consumption through automated simulations. Jiang et al. [110] analyzed the influencing factors and mechanisms of user energy behavior on building energy consumption, utilizing DeST software to estimate building energy consumption under various energy behavior scenarios, thereby exploring the influence of energy behavior on building energy consumption. Zhang et al. [111] conducted an analysis of energy behavior in university dormitories based on questionnaire surveys, proposing optimization measures such as energy-saving renovations and equipment upgrades from the perspective of energy optimization. Jiang et al. [112] introduced a smart building energy behavior prediction method based on GA-improved long short-term memory–back-propagation (LSTM-BP) neural networks, enabling the stable and accurate prediction of building energy behavior by analyzing energy usage patterns for electricity, natural gas, cooling, heating, and other energy sources. This method also facilitated the estimation of operational energy consumption for regional integrated energy systems.

3.2.4. Methods Based on Field Surveys for Calculation

Field survey involves conducting on-site investigations based on research objectives to obtain first-hand information and data on building energy use. Survey methods include questionnaire surveys and on-site testing. Field surveys ensure the accuracy of building energy consumption data, but they involve a significant workload, so typically, buildings with typical characteristics are selected for sampling surveys.

The main research approach of this calculation model is to understand the operational energy consumption intensity per unit area of different types of buildings through field surveys. This information is then combined with the area of different types of buildings published by authoritative departments to ultimately obtain the operational energy consumption of buildings within a region.

Zhou et al. [113] conducted household surveys to measure the energy consumption of existing residential buildings in Ningbo City, obtaining monthly average electricity consumption per household, gas consumption, and household water usage as indicators of the buildings’ operational energy consumption. Wang et al. [114] conducted field surveys on the energy use of several hotel buildings in Shanghai, gaining preliminary insights into the overall energy consumption patterns of hotel buildings in Shanghai. Zheng et al. [115] focused their research on existing residential buildings in cold regions, conducting field surveys to assess the energy consumption status of residential buildings in Qinhuangdao, Xingtai, Baoding, and Xinji. Jiang et al. [116] conducted household surveys to identify the driving factors of energy consumption in plateau urban households, analyzing the energy consumption characteristics of community households in Xining City and revealing spatial distribution patterns of energy consumption in high-altitude urban communities. Chou et al. [117] conducted on-site inspections of university buildings, recording and calculating the actual energy consumption of campus buildings. Liu et al. [118] conducted on-site measurements of building operational energy consumption in a cold region of China, emphasizing the significant role of zero-carbon buildings in reducing energy consumption and carbon emissions, and providing recommendations for optimizing the operation of zero-carbon buildings.

The method of energy consumption calculation based on field surveys involves selecting various types of buildings with typical characteristics within a region for sampling surveys. By obtaining the energy consumption per unit area (building operational energy consumption) from these sampled buildings and then multiplying it by the total area of each building type within the region, the overall operational energy consumption for the entire region is estimated. Compared to methods based solely on end-use energy consumption, this field-survey-based method considers more factors influencing building operational energy consumption. It can highlight the unique characteristics of different regions, thus having a greater impact on the planning of regional comprehensive energy systems. However, there are potential sources of error associated with this method, such as sampling bias in selecting survey samples or limited samples of certain building types within the region. These factors can result in larger errors in the energy consumption estimation. Therefore, this method is more suitable for larger regions where a diverse range of building types can be adequately sampled and represented.

Research related to energy consumption disaggregation in residential buildings plays a crucial role in estimating both the overall energy consumption and the usage of multiple energy sources. It serves as a pivotal component of demand-side research for regional integrated energy systems.

4. Prospects of Energy Consumption Calculation of Civil Buildings in RIESs

4.1. Application Prospects of Energy Consumption Calculation of Civil Buildings in RIESs

Calculating operational energy consumption for civil buildings is pivotal in the planning of regional integrated energy systems, offering diverse prospects for application. This estimation furnishes essential data to support regional energy system planning. Precise measurement and analysis of energy usage in civil buildings facilitate a comprehensive understanding of the primary sources and distribution patterns of energy consumption. Such understanding equips planners with valuable insights to assess urban energy demands, optimize energy supply structures, and enhance energy utilization efficiency. For example, by analyzing energy consumption patterns across different building types, planners can tailor energy-saving policies and measures, effectively promoting energy-efficient renovations and construction optimizations.

Moreover, estimating operational energy consumption in civil buildings can inform the optimization and adjustment of energy supply structures. Through analysis of building energy consumption data, planners can strategically position energy facilities and networks, optimizing supply structures to improve energy provision efficiency and reliability. For instance, in RIES planning, allocation of supply facilities for various energy types, both traditional and renewable, can be based on building energy consumption patterns to meet diverse energy demands.

Furthermore, estimating operational energy consumption in civil buildings offers crucial references for scheduling and operating an RIES. Real-time monitoring and analysis of building energy consumption data enable precise prediction and regulation of energy demand, thereby improving the alignment of energy supply and demand and reducing system energy costs. Integrating building energy consumption data with weather forecasts allows for the intelligent regulation of building energy usage, optimizing energy allocation and scheduling to enhance system operational efficiency and stability.

Estimating operational energy consumption in civil buildings holds significant promise for regional integrated energy systems planning. With ongoing technological advancements and refinement, building energy consumption estimation will provide even more precise data support and serve as a foundational basis for constructing intelligent, efficient, and sustainable urban energy systems in the future.

4.2. Implications of Calculating the Energy Consumption of Civil Buildings for the Sustainable Development of Urban Energy

The estimation of operational energy consumption in civil buildings is pivotal for fostering sustainable urban energy development. This review underscores its substantial contributions to realizing urban energy sustainability objectives, encompassing the following facets:

Data support provision: Operational energy consumption calculation in civil buildings offers indispensable data support for urban energy management. Accurate calculation and analysis of energy usage in civil buildings facilitate a comprehensive grasp of actual energy consumption patterns within cities, pinpointing peak consumption periods and high-energy-consumption zones. This serves as a scientific foundation for urban energy planning and management, facilitating the formulation of targeted energy policies and measures.

Promotion of energy efficiency and emissions reduction: Investigating operational energy consumption calculation aids in identifying peak energy consumption periods and potential energy-saving opportunities, thus fostering energy efficiency and emissions reduction in buildings. Analysis of building energy consumption data allows for the identification of high-energy-consumption buildings and abnormal energy usage behaviors, guiding owners and managers to implement effective energy-saving measures, thereby curbing building energy consumption and carbon emissions.

Optimization of energy supply: Operational energy consumption calculation in civil buildings offers crucial insights into urban energy supply. Analysis of building energy consumption data enables the precise prediction and regulation of urban energy demand, contributing to the optimization of energy supply structure and operation. This can enhance energy utilization efficiency, mitigate energy wastage, reduce supply costs, and propel urban energy development towards cleanliness, efficiency, and sustainability.

Support for intelligent development: Research on operational energy consumption calculation in civil buildings fosters the intelligent evolution of urban energy systems. Through the integration of advanced monitoring, control, and prediction technologies, real-time monitoring and management of urban energy systems become attainable, enhancing system responsiveness and flexibility. This facilitates better adaptation to dynamic changes and challenges in energy supply and demand, propelling urban energy systems towards intelligence and digitization.

Research on operational energy consumption calculation in civil buildings assumes a pivotal role in advancing urban energy sustainability objectives. Future endeavors should focus on further fortifying research and the application of operational energy consumption calculation technologies in civil buildings, continuously enhancing data accuracy and intelligence levels and thus furnishing robust support for the efficient utilization and sustainable development of urban energy.

4.3. Future Research Directions and Development Trends

The future trajectory of research concerning the calculation of operational energy consumption in civil buildings within regional integrated energy systems is poised to navigate several significant avenues and developmental trends. The following outlines prospective directions for future investigation:

Data intelligence: The ongoing advancement of the Internet of Things, big data, and artificial intelligence technologies will focus on the intelligent processing of operational energy consumption data in civil buildings. Utilizing sophisticated sensor technology and data mining algorithms, the real-time monitoring, analysis, and prediction of building energy consumption data can be achieved. This advancement will bolster data accuracy and reliability, furnishing more precise support for the optimization and scheduling of energy systems.

Model optimization: Future endeavors will concentrate on establishing more intricate and precise building energy consumption models. This necessitates a comprehensive analysis and modeling of building energy consumption, integrating various factors such as building structure, materials, and equipment. Through model refinement, building energy consumption can be more accurately forecasted, providing a more robust scientific foundation for energy system planning and management.

Energy efficiency improvement: In the future, building energy consumption measurement shows great development potential in improving energy efficiency. Through refined energy consumption measurement technology, energy consumption hotspots and inefficient links in buildings can be more accurately identified and quantified, thus providing a scientific basis for formulating targeted energy-saving measures. Detailed energy audits and post-occupancy evaluations will help identify specific opportunities for energy efficiency improvements and drive the continuous optimization of building performance. Life-cycle assessment methodologies will provide a comprehensive understanding of energy impacts throughout a building’s life span, promoting sustainable design and operations. These efforts will drive the creation of high-performance, energy-efficient civil buildings, contributing to overall sustainability and resilience in urban environments.

Interdisciplinary collaboration: Future research will intensify collaboration and communication across various disciplinary fields to achieve interdisciplinary integration and synergy. Calculating building energy consumption entails the involvement of multiple disciplinary fields such as architecture, energy science, and computer science, necessitating the comprehensive application of diverse technical means and methodologies. Through interdisciplinary collaboration, the latent value of building energy consumption data can be more effectively harnessed, fostering innovation and advancement in energy systems.

Intelligent control and management: Subsequent research will emphasize the development and application of intelligent control and management technologies. Introducing advanced control algorithms and intelligent systems will enable real-time monitoring, regulation, and optimization of building energy consumption. This will empower energy systems with greater flexibility and responsiveness, enabling adaptation to dynamic shifts in energy demand and facilitating efficient utilization and sustainable development of energy.

Integration of new energy technologies: As new energy technologies continue to emerge and evolve, forthcoming research will pivot towards integrating these innovations with building energy consumption calculation. For instance, integrating technologies like solar photovoltaics and wind power generation with building energy consumption data can facilitate self-sufficiency and the renewable utilization of building energy, offering novel approaches for constructing low-carbon and environmentally friendly urban energy systems.

The future trajectory of research on operational energy consumption in civil buildings will center on facets including data intelligence, model optimization, interdisciplinary collaboration, intelligent control and management, and integration of new energy technologies. Only through continuous innovation, methodological refinement, and the amalgamation of theory and practice can we effectively advance and apply operational energy consumption calculation technology in civil buildings, thereby providing robust support for the construction of intelligent, efficient, and sustainable urban energy systems.

5. Conclusions

Energy consumption calculation of civil buildings is a key component for understanding and optimizing RIESs. This paper explores the characteristics of the energy consumption of civil buildings, the method of energy consumption calculation and its prospective applications in RIESs. The energy consumption of civil buildings occupies a significant proportion of urban energy consumption. It is affected by factors such as climate, building orientation, building envelope and occupant behavior. Different types of civil buildings, including office buildings, shopping malls, hospitals, hotels, and residential buildings, exhibit distinct energy consumption characteristics. Given the necessity for RIESs to manage the energy in the whole region, it is crucial to accurately calculate the energy consumption of various types of civil buildings in the RIES in question.

On the calculation method of building energy consumption, this paper introduces two methods: top–down methods and down–top methods. Top–down methods for energy consumption calculation include methods based on statistical yearbooks, energy balance sheets, and end-use energy consumption, while down–top methods are based on sub-metering system design, analysis of influencing factors of operating energy consumption, analysis of energy use behavior, and field surveys. These methods have broad application potential in RIESs.

Accurate energy consumption calculation of civil buildings can provide important reference for the energy management of RIESs, facilitate improvements in the energy supply structure of RIESs, and enhance the efficiency and reliability of energy supply in RIESs. Looking at future development, this paper suggests that research on the energy consumption calculation of civil buildings can be further developed from the aspects of data intelligence, model optimization, interdisciplinary collaboration, intelligent control management, new energy technology integration, etc., so as to promote the continuous optimization and progress of RIESs.