Advancing AI-Enabled Techniques in Energy System Modeling: A Review of Data-Driven, Mechanism-Driven, and Hybrid Modeling Approaches

Abstract

Artificial intelligence (AI) is increasingly essential for optimizing energy systems, addressing the growing complexity of energy management, and supporting the integration of diverse renewable sources. This study systematically reviews AI-enabled modeling approaches, highlighting their applications, limitations, and potential in advancing sustainable energy systems while offering insights and a framework for addressing real-world energy challenges. Data-driven models excel in energy demand prediction and resource optimization but face criticism for their “black-box” nature, while mechanism-driven models provide deeper system insights but require significant computation and domain expertise. To bridge the gap between these approaches, hybrid models combine the strengths of both, improving prediction accuracy, adaptability, and overall system optimization. This study discusses the policy background, modeling approaches, and key challenges in AI-enabled energy system modeling. Furthermore, this study highlights how AI-enabled techniques are paving the way for future energy system modeling, including integration and optimization for renewable energy systems, real-time optimization and predictive maintenance through digital twins, advanced demand-side management for optimal energy use, and hybrid simulation of energy markets and business behavior.

1. Introduction

Artificial intelligence (AI) is becoming a crucial driver of the energy transition, particularly as countries strive to meet global climate targets and work toward carbon neutrality. In response to the urgent need for sustainable solutions, nations have set ambitious goals, such as promoting renewable energy [1,2]. As this transition progresses, the complexity of managing energy systems intensifies, necessitating advanced AI-enabled methods for system optimization and decision-making [3,4]. The power of AI and other digital technologies, such as big data analytics, lies in its ability to analyze vast amounts of data, transforming raw information into actionable insights that aid real-time monitoring, demand–supply forecasting, and dynamic system adjustments [5,6].

Key to AI’s impact in the energy sector are advanced modeling techniques that bridge data and actionable intelligence, including data-driven, mechanism-driven, and hybrid modeling approaches in energy systems [7,8]. Data-driven modeling, leveraging large datasets through machine learning and deep learning algorithms, excels at predicting changes in energy demand and optimizing resource allocation [9,10]. For example, time series analysis and regression models enable researchers to identify key factors influencing energy demand and make timely adjustments [11,12]. Mechanism-driven models, rooted in fundamental physical laws, provide a more foundational understanding of system behavior, excelling at simulating energy conversion processes [13,14]. Both data-driven and mechanism-driven models have made progress in energy system modeling, but each has limitations [15,16]. Data-driven models often treat the system as a “black box”, focusing on data without explaining underlying mechanisms, which can lead to hard-to-interpret results [17]. Mechanism-driven models, based on physical laws, struggle with complex and uncertain data, making computation intensive [18]. In this context, hybrid modeling methods have emerged to integrate both data-driven and mechanism-driven methods, combining the predictive power of AI with the reliability of physics-based models [19]. These hybrid models enhance accuracy and adaptability, allowing for a more comprehensive understanding of complex energy systems. Despite the potential of these approaches, challenges remain, particularly in the effective integration of diverse data sources with physical modeling frameworks, underscoring a need for continued research and innovation [20].

To address this topic, this study provides a timely and systematic review of AI-enabled modeling approaches to advance sustainable energy systems, focusing on data-driven, mechanism-driven, and hybrid models. By synthesizing current literature, it highlights the practical applications, limitations, and future potential of these methodologies within diverse energy scenarios. The research bridges theoretical advancements and real-world applications, revealing how AI supports energy system modeling to address sustainability challenges. Additionally, it offers valuable insights and a framework for future research, guiding the development of innovative solutions to real-world energy challenges.

This study is organized as follows: first, an overview of global policies relevant to AI-enabled energy solutions; second, an analysis of modeling approaches with a focus on data-driven, mechanism-driven, and hybrid methods; third, a discussion of future pathways for technological and policy advancements in AI for energy; and finally, a conclusion summarizing the findings and their implications. This review aims to offer a systematic perspective for advancing research and practice in AI applications within energy systems.

2. Global Policy Review for AI in Energy Systems

As countries worldwide pursue ambitious sustainability and climate targets, the digitalization of energy systems is becoming increasingly essential. This transformation allows for efficient energy distribution, real-time monitoring, and dynamic demand management—key capabilities in managing rising global energy demands and facilitating the integration of renewable resources. Through advanced technologies such as AI, big data, and the Internet of Things (IoT), digitalized energy systems enable smart grids, predictive maintenance, and optimized resource use, which collectively reduce waste, enhance grid stability, and improve overall energy efficiency.

To support these objectives, major economies have developed specific policies and frameworks aimed at modernizing their energy sectors with digital technology. The European Union, the United States, and China, in particular, are at the forefront of digital innovation in energy. Their policies focus on leveraging digital tools such as AI-driven smart grids, predictive algorithms, and data interoperability standards. These measures help manage energy resources more effectively, engage consumers in energy use decisions, and ensure cybersecurity, which is increasingly critical in an interconnected energy landscape.

Table 1. An overview of global policy framework for AI in energy systems in major developed and developing economies.

Country	Policy Name	Key Contents
European Union	Action Plan on Digitalising the Energy System [21]	- Supports digital energy ecosystem and data exchange.
		- Focuses on cybersecurity and data interoperability.
		- Promotes digital tools for energy efficiency and use of renewable energy.
	European Green Deal [22]	- Targets net-zero emissions by 2050 with digital energy solutions.
		- Invests in smart grids and renewable integration.
		- Supports sustainable energy transition.
	Fit for 55 Package [23]	- Aims for 55% emissions reduction by 2030 through digital technology.
		- Promotes energy efficiency and renewable integration.
		- Focuses on smart meters and grid modernization.
United States	Bipartisan Infrastructure Law [24]	- Allocates funds for grid resilience and digital technology adoption.
		- Supports AI-driven grid solutions.
		- Invests in smart grid and renewable hubs.
	Energy Act of 2020 [25]	- Encourages AI for grid optimization.
		- Focuses on energy system cybersecurity.
		- Supports digital energy research.
	Inflation Reduction Act [26]	- Incentivizes digital and clean energy technologies.
		- Promotes energy efficiency and smart buildings.
		- Supports clean technology adoption.
India	National AI Strategy (NITI Aayog) [27]	- Focuses on AI for renewable energy forecasting and grid management.
		- Supports smart grids for energy efficiency and sustainability.
		- Promotes public–private collaboration in AI-driven energy innovation.
Brazil	National AI Strategy (Estratégia Brasileira de IA) [28]	- Applies AI for energy efficiency and renewable energy management.
		- Optimizes hydroelectric power, a key energy source in Brazil.
		- Promotes AI research to enhance energy grid resilience and reliability.
		- Supports AI integration into smart grids for improved energy distribution.
China	14th Five-Year Plan for Energy Development [29]	- Targets digital and smart grid development.
		- Encourages AI, IoT, and big data in energy.
		- Aims to modernize energy systems and integrate renewables.
	14th Five-Year Plan for Digital Economy Development (2021–2025) [30]	- Strengthens digital infrastructure for smart grids.
		- Promotes AI, big data, and cloud computing in energy.
		- Aims to boost energy efficiency and reduce emissions
	State Council’s Action Plan for Peak Carbon Emissions by 2030 [31]	- Uses digital tools for energy monitoring.
		- Enhances efficiency and cuts carbon footprints.
		- Targets increased renewable energy and decarbonization via digitalization.

below provides a comparative overview of these key policies in major developed and developing economies, highlighting each country’s approach to AI, smart grid technology, data integration, and cybersecurity. Each framework is tailored to the region’s specific energy transition goals, emphasizing enhanced grid efficiency, renewable resource integration, and carbon emission reduction. Together, these policies represent a collaborative and technologically driven approach to advancing energy sustainability on a global scale.

3. Main Modeling Approaches in Energy System Modeling

This section explores various AI-enabled energy system modeling approaches, emphasizing their role in optimizing energy production, transformation, and consumption. These approaches can be broadly classified into three categories: data-driven, mechanism-driven, and hybrid methods. Data-driven approaches leverage machine learning techniques such as supervised and unsupervised learning, reinforcement learning, and deep learning to analyze and predict energy system behavior. Mechanism-driven models focus on understanding and simulating energy flows, optimizing energy use, and solving energy system-related problems based on physical laws. Hybrid models integrate elements from both data-driven and mechanism-based methods, with applications in renewable energy integration, smart grid operations, energy demand forecasting, and intelligent building systems. Together, these approaches provide a comprehensive toolkit for tackling modern energy system challenges.

3.1. Data-Driven Energy System Modeling

Data-driven modeling harnesses historical data and advanced algorithms to uncover patterns and make accurate predictions, often without relying on a system’s underlying physical properties. This approach is particularly valuable for addressing complex, non-linear systems where traditional, parameter-based models may fall short due to their reliance on precise prior knowledge and predefined assumptions [32]. Data-driven models are highly effective in environments where detecting hidden patterns and relationships is essential, offering exceptional accuracy when trained on large, diverse datasets. Moreover, these models are adaptive, continuously improving as new data are incorporated, making them an indispensable tool for dynamic forecasting and informed decision-making in the rapidly evolving landscape of modern energy systems [33].

Machine learning algorithms play a pivotal role in data-driven energy system modeling, enabling systems to learn from data and make predictions or decisions without explicit programming. The primary categories of machine learning techniques used in energy system modeling include supervised learning, unsupervised learning, reinforcement learning, and deep learning (

Table 2. Main machine learning algorithms in data-driven energy system modeling.

	Supervised Learning	Unsupervised Learning	Reinforcement Learning	Deep Learning
Input Data	Labeled data	Unlabeled data	States and reward feedback	Complex high-dimensional data
Objective	Learn the mapping from input to output	Discover patterns in data	Find the optimal strategy for maximizing reward	Learn complex features
Data Interaction	Static	Static	Dynamic (interaction with the environment)	Static or dynamic
Core Algorithms	Linear regression; SVM; Decision Trees	K-means; PCA	Q-learning; policy gradient	CNN; MLP; LSTM
Typical Tasks	Classification; regression	Clustering; dimensionality reduction	Dynamic optimization and control	Analyzing large, complex datasets for prediction, classification, and optimization
Main Applications in Energy Systems	Energy consumption forecasting; demand response	Anomaly detection; energy efficiency optimization	Energy scheduling; resource allocation	Energy prediction; fault diagnosis; renewable energy integration
Limitations	Requires labeled data; sensitive to overfitting.	Results are harder to evaluate; lacks interpretability.	Computationally expensive; slow convergence.	Needs large datasets; lacks transparency; resource-intensive.

) [34]. Supervised learning algorithms, such as regression and classification models, are used to predict outcomes based on labeled data. Unsupervised learning, including clustering and dimensionality reduction techniques, helps identify hidden patterns and structures in unlabeled data. Reinforcement learning is applied to optimize decision-making in dynamic environments, where agents learn through trial and error to maximize rewards over time. Deep learning, a subset of machine learning, utilizes complex neural networks to model highly intricate relationships in large datasets, making it particularly powerful for tasks such as energy demand forecasting and optimizing energy distribution [35].

3.1.1. Supervised Learning

Supervised learning is a fundamental branch of machine learning that relies on labeled data to train algorithms, enabling them to map inputs to outputs and make accurate predictions based on learned relationships. Its defining feature is a structured approach that uses input-output pairs to train the model, ensuring reliability and interpretability for clear decision-making tasks [36,37,38]. Popular supervised learning methods include linear regression, Support Vector Machines (SVM), Decision Trees, Random Forests, Logistic Regression, and k-Nearest Neighbors (k-NN) [39]. These methods offer diverse capabilities, from classification and regression to predictive analytics, making supervised learning an adaptable and powerful tool in various domains.

Supervised learning plays a crucial role in the energy sector, primarily focusing on prediction tasks. For example, supervised learning algorithms can analyze historical data from energy consumption patterns to forecast future demand, allowing energy providers to adjust supply in real time and improve grid stability [40,41]. Additionally, for renewable energy, such as wind and solar, supervised learning can predict energy output based on weather conditions, helping to optimize energy storage and distribution [42]. Furthermore, it aids in energy efficiency by identifying inefficiencies in consumption patterns and suggesting improvements for both industrial and residential users [43,44,45]. By leveraging labeled datasets, such as energy consumption data or sensor readings, supervised learning models can enhance the overall efficiency and sustainability of the energy sector, driving innovation in smart grids and sustainable energy solutions.

Despite its strengths, a key challenge is the reliance on high-quality labeled data, which can be expensive and time-consuming to obtain in many energy applications [46]. Moreover, while supervised learning performs well on small to medium-sized datasets, it can struggle with scalability for very large datasets, as its computational complexity increases with the size of the dataset [47]. This makes it less ideal for scenarios involving vast quantities of real-time energy data. Furthermore, supervised learning models may sometimes lack flexibility in adapting to dynamic changes, requiring retraining to accommodate evolving systems or patterns.

3.1.2. Unsupervised Learning

Unsupervised learning is a branch of machine learning that identifies patterns and relationships within unlabeled datasets, making it particularly useful for exploratory data analysis and data-driven decision-making. Its defining characteristic is its ability to autonomously uncover hidden structures, such as clusters or anomalies, without relying on predefined labels or prior knowledge of the data [48]. This adaptability allows unsupervised learning to reveal insights that might otherwise go unnoticed, making it invaluable for understanding complex and unstructured datasets. Key methods in this category include K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), Autoencoders, Gaussian Mixture Models (GMM), and t-Distributed Stochastic Neighbor Embedding (t-SNE) [49,50].

Unsupervised learning is a powerful tool in the energy sector, offering solutions for complex problems where labeled data are scarce. It excels in tasks such as clustering, anomaly detection, and dimensionality reduction. For example, it can be used to detect faults in power systems and equipment by identifying anomalies in operational data, which helps in preventive maintenance and improving system reliability [51]. In renewable energy systems, unsupervised algorithms are used to analyze weather patterns and categorize similar energy production scenarios, enhancing the efficiency of solar and wind energy forecasting [52]. Additionally, it enables energy providers to segment customers based on their consumption behavior, allowing for personalized energy-saving recommendations and demand-side management strategies. By discovering hidden patterns in large datasets, unsupervised learning drives advancements in predictive maintenance and operational optimization, contributing to a more reliable and sustainable energy infrastructure [53].

The primary advantages of unsupervised learning are its simplicity and scalability. These features make it well-suited for analyzing large-scale energy datasets, where its speed and efficiency allow for quick pattern discovery and actionable insights. However, unsupervised learning methods requires the number of clusters to be predefined, which may be challenging in situations where the optimal number of groups is unknown [54]. Additionally, it is sensitive to the initial placement of cluster centroids and can struggle with datasets containing clusters of varying sizes or non-spherical shapes.

3.1.3. Reinforcement Learning

Reinforcement learning is a unique branch of machine learning where algorithms learn to make sequential decisions by interacting with their environment [55]. Unlike supervised or unsupervised learning, reinforcement learning is based on a trial-and-error process, where the algorithm receives feedback in the form of rewards or penalties to refine its strategy over time. The defining feature of reinforcement learning is its adaptability, making it particularly suitable for dynamic, uncertain environments where conditions evolve continuously. Main methods, such as Q-Learning, Deep Q-Learning, Policy Gradient Methods, and Actor-Critic Models, are widely used for problems requiring real-time decision-making and long-term optimization [56,57].

The main application of reinforcement learning in the energy sector is optimizing energy management and control systems, particularly in areas such as demand response, renewable energy integration, smart grid management, and battery storage optimization. Its ability to learn through trial-and-error interactions with complex environments makes it highly suitable for dynamic and stochastic energy systems. For example, reinforcement learning is widely used in smart grid operations to balance supply and demand in real time [58]. It can optimize the dispatch of distributed energy resources, coordinate demand-side management strategies, and minimize operational costs while maintaining grid stability. In renewable energy systems, reinforcement learning helps manage intermittency by optimizing the scheduling and storage of energy in battery systems, ensuring that energy generated from sources such as solar or wind is efficiently stored and utilized [43]. Additionally, reinforcement learning is employed in energy-efficient building management, where it learns optimal heating, ventilation, and air conditioning (HVAC) settings to minimize energy consumption while maintaining occupant comfort [59,60].

The key advantage of reinforcement learning is its ability to handle dynamic, multi-stage decision-making problems without requiring explicit models of the environment. Its capacity to adapt and optimize in real time makes it an excellent choice for complex energy systems with variable renewable energy inputs and shifting demand patterns. However, the main limitation of reinforcement learning in the energy sector is its reliance on extensive trial-and-error learning, which can be computationally expensive and time-consuming, especially in systems where real-world experimentation is not feasible [61]. Reinforcement learning requires a large quantity of data and interactions with the environment to learn optimal policies, which poses challenges in the energy sector, where certain actions, such as grid instability or equipment damage, have high stakes and cannot be tested freely.

3.1.4. Deep Learning

Deep learning is a specialized subset of machine learning that uses multilayered neural networks to extract meaningful insights from large, complex datasets. Its defining characteristic is the ability to automatically learn representations from raw data, significantly reducing the need for manual feature engineering [62,63]. This adaptability makes deep learning particularly well suited for handling diverse and unstructured data types. Deep learning encompasses a range of methods, including Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), each designed for specific types of tasks and data structures [64].

The hallmark feature of deep learning is its ability to model highly non-linear and complex relationships. CNNs, for instance, excel at processing spatial data and are widely used for analyzing images and grids [64,65,66]. In the energy sector, CNNs are applied to tasks such as solar panel monitoring, where they detect faults and anomalies by analyzing drone or satellite images. CNNs are also used in identifying damages to transmission lines and assessing the condition of other critical energy infrastructure. General-purpose models such as ANNs [67] and MLPs [68] are more versatile and have been extensively used in energy demand forecasting, power grid optimization, and resource allocation. Their layered architecture enables these models to capture complex interactions among multiple variables.

Time-series-specific models, such as LSTMs and GRUs, are particularly well suited for sequential data and have become indispensable in energy applications requiring temporal analysis. LSTMs [69], with their capability to capture long-term dependencies, are widely used for renewable energy generation forecasting, such as predicting solar or wind output based on historical data. GRUs [70], which offer a computationally efficient alternative to LSTMs, are ideal for short-term electricity load prediction and real-time anomaly detection in smart grids. Both LSTMs and GRUs excel in scenarios requiring continuous monitoring and analysis of energy data streams.

Despite their strengths, a primary limitation is their reliance on large labeled datasets for training, which can be resource-intensive to obtain in the energy domain [71]. Additionally, the “black-box” nature of deep learning models often makes their decision-making process opaque, raising concerns about interpretability in critical energy applications [72]. These models also require significant computational resources, which can be a barrier for real-time deployment in large-scale energy systems.

3.2. Mechanism-Driven Energy System Modeling

Mechanism-driven models are rooted in the physical laws governing energy systems, such as thermodynamics, using mathematical representations to simulate system behavior. By incorporating real-world parameters and physical constraints, they enable precise evaluations of processes such as energy production, conversion, consumption, and storage. These models provide accurate insights into the interactions and performance of components, making them essential for analyzing system stability, efficiency, and optimization strategies. While highly effective for understanding and improving energy systems, they rely heavily on accurate input data and can be computationally intensive for large-scale or complex scenarios. These methods can broadly be categorized into energy flow models, energy simulation models, and energy optimization models (

Table 3. Main mechanism-driven models for energy system modeling.

	Energy Flow Models	Energy Simulation Models	Energy Optimization Models
Description	Tracks energy flows and losses across production and use	Simulates energy system operations and behavior	Optimizes energy systems for cost, efficiency, and policy
Key Techniques/Tools	LEAP; EFA; Sankey diagrams	Aspen Plus; Thermoflex	LP; NLP; MILP; GAMS
Applications	Energy planning; industrial efficiency; building analysis	Power plants; microgrids; building energy retrofits	Grid planning; renewable integration; demand response
Strengths	Identifies inefficiencies; supports sustainability	High precision; real-time evaluations	Prescriptive; handles trade-offs; aligns with goals
Limitations	Limited scope; ignores economic/social factors	Computationally intensive; input-dependent	Sensitive to input and uncertainties; resource-heavy

).

3.2.1. Energy Flow Models

Energy flow models focus on tracking and analyzing the movement of energy through various stages of an energy system, including energy production, transformation, and consumption (Figure 1). These models offer a systematic framework to evaluate energy flow pathways, providing detailed insights into how energy is transformed and utilized at each stage. Energy production typically involves harnessing primary energy sources such as fossil fuels, nuclear energy, or renewable resources such as solar, wind, and hydropower [73]. The harvested energy undergoes transformation processes, where energy input is converted into more usable forms, such as electricity, refined fuels, or mechanical energy. During these transformations, inefficiencies and energy losses, often in the form of heat, are inevitable due to thermodynamic limits and system inefficiencies. Finally, energy consumption occurs across the industrial, transportation, and building sectors, each characterized by specific energy demands and usage patterns. By systematically mapping and analyzing these stages, energy flow models are invaluable in identifying inefficiencies, quantifying energy losses, and pinpointing opportunities to optimize energy performance. For instance, they can highlight the potential benefits of adopting cleaner energy sources, improving energy transformation efficiency, or implementing advanced technologies to reduce waste [74,75]. As such, these models serve as a cornerstone of energy system analysis, guiding policy decisions, technological advancements, and sustainability efforts aimed at enhancing energy efficiency and reducing related CO₂ emissions.

Energy flow modeling techniques are frequently implemented using robust frameworks such as the LEAP (Long-range Energy Alternatives Planning) model, a highly versatile tool that enables in-depth analysis of energy production, conversion, and consumption across a range of sectors [76,77,78,79]. LEAP excels in forecasting future energy scenarios, providing policymakers, researchers, and planners with valuable insights into the potential impacts of various policy measures, technological innovations, and economic growth trajectories. Its ability to model both demand- and supply-side dynamics makes it a comprehensive and effective framework for addressing pressing challenges related to sustainability, energy security, and climate change mitigation. By simulating different energy pathways, LEAP supports decision-making processes aimed at achieving long-term environmental and societal goals.

Another widely employed technique is Energy Flow Analysis (EFA), a method that rigorously quantifies energy inputs, outputs, conversions, and losses within a specific system boundary, such as a building, city, industrial facility, or regional energy network [80,81,82]. EFA provides a detailed accounting of energy transformations, helping researchers and practitioners identify inefficiencies, assess energy performance, and evaluate the effectiveness of interventions aimed at improving energy utilization. This method is particularly valuable in uncovering hidden opportunities for optimizing energy flows, minimizing waste, and accelerating the transition to cleaner, more sustainable energy systems. With its ability to break down energy flows into actionable insights, EFA plays a critical role in guiding targeted investments, enhancing operational efficiencies, and ensuring progress toward long-term sustainability and carbon reduction targets.

Visual tools such as energy Sankey diagrams are extensively used to represent energy flows in an intuitive and easily interpretable format, where the width of arrows corresponds to the magnitude of energy inputs, outputs, losses, and conversions (Figure 2) [83,84]. These diagrams provide a clear visualization of complex energy systems, making them accessible to a wide range of stakeholders, from policymakers to engineers, while effectively identifying inefficiencies and areas in need of improvement. Commonly used in applications such as industrial energy management, national energy planning, and building performance analysis, Sankey diagrams serve as powerful communication tools that facilitate decision-making and policy formulation. They are often integrated within larger energy modeling frameworks, allowing for dynamic scenario analysis and helping stakeholders visualize the potential outcomes of different energy strategies and interventions.

Additionally, energy flow models often simplify system interactions by focusing solely on energy flows, neglecting critical aspects such as economic activities, supply chain dynamics, societal behavior, and environmental impacts. This narrow scope can limit their ability to provide a complete picture of energy systems, especially when addressing multifaceted challenges such as sustainability or policy implications. To overcome these shortcomings, energy flow models are often integrated with complementary approaches such as Input-Output (I-O) Analysis [85,86], which links energy flows to economic activities, and Material Flow Analysis (MFA) [87,88], which incorporates resource usage and environmental impacts. These integrations enhance the depth and breadth of analysis, offering a more holistic understanding of energy systems and ensuring more effective and sustainable decision-making.

3.2.2. Energy Simulation Models

Energy simulation models replicate energy systems’ operational behavior using fixed rules and algorithms, accurately representing energy flows, physical characteristics, and dynamic interactions [89,90]. They are crucial in designing and evaluating energy projects, allowing researchers to test scenarios, refine configurations, and predict outcomes. Typically used in advanced development stages, these models focus on optimizing system parameters for maximum efficiency. By applying physical rules and parameters such as heat transfer, material properties, and weather conditions, they enable real-time or near-real-time performance evaluation for tasks such as power plant efficiency, microgrid operations, and building energy consumption.

Energy simulation models are extensively employed in the refining design of individual energy systems, such as thermal power plants (Figure 3), where they play a crucial role in optimizing performance and operational efficiency [91,92,93]. These models are used to analyze the efficiency of key components, including steam turbines, boilers, condensers, and cooling systems, all of which are integral to energy conversion and production. Software tools such as Aspen Plus and Thermoflex simulate the thermodynamic processes in these plants, allowing engineers to model and optimize fuel consumption, energy output, and emissions. By incorporating advanced algorithms, these tools provide insights into the optimal operation of each component under various conditions, helping to reduce waste and improve overall efficiency. Moreover, thermal power plant simulation models are indispensable for retrofitting older plants to meet stricter environmental regulations, such as reducing carbon emissions, or for integrating Carbon Capture and Storage (CCS) technologies [94]. Through detailed simulations, these models support the transition to greener energy practices by identifying opportunities for emission reductions and improved fuel efficiency, ensuring plants comply with evolving environmental standards while maximizing energy production.

Another prominent application of energy simulation models is in the design and optimization of microgrids (Figure 4). These localized energy systems combine renewable energy sources, energy storage, and traditional power generation units to create self-sustaining, resilient grids capable of operating independently from the central grid when necessary [95,96,97]. These models simulate how various energy resources can be integrated to meet local demand, balance energy supply, and ensure system reliability during power outages or other disruptions. By assessing how renewable sources such as solar or wind can be paired with energy storage systems and traditional backup generation, these models enable stakeholders to make data-driven decisions that reduce reliance on fossil fuels and enhance energy security. Additionally, microgrid simulations are crucial for communities and industries seeking to enhance energy independence and sustainability, offering a pathway to reduce grid dependency while promoting clean energy integration.

Energy simulation models are also widely used in building energy modeling, where they simulate and optimize energy usage in residential and commercial buildings. These models focus on a variety of energy flows, including heating, cooling, lighting, ventilation, and HVAC (Heating, Ventilation, and Air Conditioning) systems [98,99,100]. These models incorporate sophisticated algorithms to simulate dynamic interactions between building components, weather conditions, and HVAC systems, allowing engineers to evaluate the performance of different building materials, insulation types, and renewable energy systems, such as rooftop solar panels, in terms of their impact on overall energy efficiency. By simulating various scenarios, such as changes in insulation or glazing materials, they help to design energy-efficient buildings that not only reduce energy consumption but also meet rigorous sustainability standards. For example, it can assess the energy performance improvements achieved through better insulation or the integration of renewable energy sources, enabling architects and engineers to create buildings that are both cost-effective and environmentally responsible.

Despite their versatility and accuracy, energy simulation models are computationally demanding, requiring significant processing power and time, especially for large or complex projects. Their accuracy depends heavily on precise and comprehensive input data, and any inaccuracies can lead to unreliable results [101,102]. Many models focus narrowly on physical and operational aspects, often excluding broader considerations such as economic factors, policies, and social behavior. This limits their ability to address complex, interconnected energy challenges, emphasizing the need for complementary approaches.

3.2.3. Energy Optimization Models

Energy optimization models are designed to identify the most efficient, cost-effective, or sustainable solutions for managing energy systems while satisfying specific objectives and constraints. A key feature of energy optimization models is their ability to handle complex trade-offs and prioritize decision-making under resource or operational constraints. For example, they can determine the optimal energy mix for a power grid, balancing renewable and non-renewable energy sources while ensuring grid stability [103]. Unlike simulation models, which replicate the behavior of a system, optimization models focus on prescribing the best course of action. These models are built around an objective function, which might aim to minimize costs, maximize energy efficiency, or reduce carbon emissions, subject to constraints such as resource availability, technological capabilities, and policy regulations.

Energy optimization models operate by solving mathematical formulations that balance objectives such as minimizing costs or reducing emissions against constraints such as resource availability, system capacities, and policy regulations [104,105]. These formulations employ advanced techniques, including linear programming (LP) for problems with linear relationships, non-linear programming (NLP) for handling complex, non-linear interactions, and mixed-integer linear programming (MILP) for scenarios involving discrete decision variables, such as selecting between operational modes or equipment. These methodologies enable energy optimization models to provide prescriptive solutions for energy system design and management.

One of the primary applications of energy optimization models is in energy supply planning (Figure 5). These models evaluate the entire energy supply chain, from resource extraction to energy end-use, to identify cost-effective pathways that align with policy goals such as reducing greenhouse gas emissions. For instance, it has been applied in forecasting the future energy mix in the energy transition of China, helping to identify optimal investment in renewable technologies and energy storage to meet net-zero emissions targets by 2060 [106]. By simulating numerous scenarios, energy supply optimization models empower policymakers to make informed decisions that balance economic, environmental, and social considerations.

Another critical application lies in power grid operation planning (Figure 6), where optimization models such as GAMS (General Algebraic Modeling System) provide actionable insights for managing electricity grids efficiently [107,108,109,110]. These models are used to determine the optimal dispatch of power plants to minimize operational costs while meeting real-time electricity demands. They also facilitate the integration of variable renewable energy sources, such as solar and wind, ensuring grid stability without over-reliance on fossil fuels. For example, GAMS has been extensively used by utilities to optimize power plant schedules, model energy storage systems, and plan transmission infrastructure expansions to reduce congestion and enhance efficiency. Additionally, these models support demand-response strategies by analyzing consumer behavior and recommending load-shifting incentives during peak hours to prevent grid overload. In cases such as extreme weather events, grid optimization models help utilities plan for contingencies, such as activating backup plants or reallocating resources to high-demand areas.

To function effectively, energy optimization models rely on detailed input data, including resource availability, energy prices, weather patterns, and system specifications. For example, accurate projections of solar irradiance or wind speeds are essential for planning renewable energy integration, while fuel price trends impact cost-minimization strategies. Given the uncertainties inherent in energy systems, sensitivity analysis is often applied to test the robustness of solutions under different scenarios. For instance, planners might evaluate how fluctuating natural gas prices affect the cost-effectiveness of renewable energy investments or explore how varying carbon taxes influence grid decarbonization strategies. This ability to account for uncertainties ensures that optimization models deliver flexible and resilient strategies, making them indispensable tools for sustainable energy system management.

3.3. Hybrid Energy System Modeling

Despite the widespread use of both data-driven and mechanism-driven models in energy system modeling, each approach has inherent limitations. Data-driven models are exceptional at identifying complex, non-linear patterns in large datasets, but they depend heavily on the availability of high-quality data, which may not always be obtainable, especially in emerging or evolving energy systems. Furthermore, these models often lack interpretability, which makes it difficult to understand the underlying processes that lead to their predictions, limiting their application in critical decision-making where transparency is essential. They also struggle to generalize to new, unseen scenarios, which can reduce their reliability in dynamic, real-world settings. On the other hand, mechanism-driven models are based on well-established physical principles, offering a clear and interpretable framework for understanding system behavior. However, these models are often constrained by the need for detailed knowledge of system parameters, making them resource-intensive and challenging to apply to systems with incomplete or uncertain data. Moreover, mechanism-driven models can be computationally expensive and less adaptable to rapidly changing environments, limiting their scalability in certain applications.

The hybrid modeling approach overcomes the limitations of both data-driven and mechanism-driven models by combining their respective strengths (Figure 7). It leverages the predictive power of data-driven models, which excel at uncovering intricate patterns and non-linear behavior from historical data, while also incorporating the interpretability and reliability of mechanism-driven models that are grounded in fundamental physical principles [111]. This integration bridges the gap between empirical insights and theoretical rigor, offering a more comprehensive and robust approach to energy system modeling. By enhancing accuracy, adaptability, and robustness, hybrid models can handle the complex, dynamic nature of modern energy systems more effectively. Applications of hybrid modeling are particularly impactful in energy demand forecasting, intelligent building systems, renewable energy integration, and smart grid operation.

3.3.1. Energy Demand Forecasting

Energy demand is non-stationary and dynamic in time. Accurate energy demand forecasting is a cornerstone of efficient energy system management, ensuring optimal resource allocation, grid stability, and cost-effective energy distribution. Hybrid models can enhance the accuracy and transparency of energy demand forecasts by integrating empirical data with theoretical insights. This combination allows for a more comprehensive understanding of the factors influencing energy demand [112].

For example, Nikseresht and Amindavar [113] presents a new adaptive hybrid approach for energy time series forecasting. This approach effectively captured nonlinear relationships in energy usage data, offering high forecasting accuracy across different timescales.

In another example, Mittakola et al. [114] uses a combination of ARIMA and ANN to forecast monthly gas demand. By incorporating real-time weather information and energy consumption patterns, this approach improves the accuracy of forecasts over weeks to months. It is crucial for energy providers and policy-makers aiming to reduce uncertainty in energy supply planning. The model’s ability to predict demand under extreme weather conditions (such as cold spells) would be particularly valuable.

Furthermore, hybrid models are employed in electric load forecasting to combine trend analysis with correlations to economic and demographic factors, enhancing the robustness of predictions across different time frames. For example, Bakare et al. [115] discusses a hybrid long-term industrial electrical load forecasting model that combines the Adaptive Neuro-Fuzzy Inference System with Gene Expression Programming. This model can effectively guide investment strategies and policies for electricity generation and consumption.

3.3.2. Intelligent Building Systems

Hybrid modeling is playing a transformative role in optimizing intelligent building systems, which are designed to improve energy efficiency, reduce operational costs, and enhance occupant comfort. By integrating the predictive capabilities of data-driven models with the interpretability and physical rigor of mechanism-driven models, hybrid approaches enable more adaptive, efficient, and intelligent building operations. This combined approach allows for enhanced control of HVAC (Heating, Ventilation, and Air Conditioning) systems, lighting, and energy storage, thereby supporting sustainable building design.

One of the most notable applications of hybrid modeling is in energy consumption prediction and control within smart buildings. Tian et al. [116] developed a hybrid digital twin-based approach for forecasting and optimizing energy usage in smart building systems. Their framework integrates data-driven real-time analytics with physical models of building energy flow. This hybrid system enables more accurate predictions of HVAC energy demands, allowing building managers to implement dynamic control strategies that reduce energy waste and improve occupant comfort.

Another significant application of hybrid modeling is adaptive control of smart HVAC systems. Xiong et al. [117] proposed a hybrid two-level energy management strategy for multi-microgrid systems, which includes intelligent buildings as part of a broader energy network. By integrating reinforcement learning with mechanism-driven modeling, their system adapts to changing energy demands in real time. This approach can be applied to HVAC systems in buildings, where energy needs shift based on occupancy and external weather conditions.

Occupant comfort prediction and optimization is another area where hybrid models are making a significant impact. To ensure a comfortable environment for building occupants, intelligent buildings must predict and control indoor air quality, temperature, and lighting. Chen et al. [118] demonstrated a hybrid demand-side management model that optimizes energy usage in industrial and commercial buildings. By forecasting demand using data-driven models and optimizing system control through physics-based models, the hybrid system ensured occupant comfort while minimizing energy use.

3.3.3. Renewable Energy Integration

The hybrid modeling approach is revolutionizing the integration of renewable energy sources by addressing critical challenges such as variability, intermittency, and optimization in energy systems. By improving forecasting accuracy, optimizing operational strategies, and ensuring grid stability, hybrid models offer a comprehensive framework for managing the complexities associated with renewable energy systems.

Hybrid modeling approaches combine AI techniques with physics-based methods to significantly improve renewable power forecasting for wind, photovoltaic, and concentrated solar power systems. Hossain Lipu et al. [119] provides a detailed review of recent progress in hybrid data-driven algorithms for predicting power from various renewable sources such as solar, wind, ocean, hydro, and geothermal. It highlights the variables, forecasting horizons, performance metrics, contributions, and limitations of these hybrid approaches.

Hybrid models also play a pivotal role in designing and optimizing standalone renewable systems. Zhang et al. [120] proposed a hybrid optimization algorithm that integrates weather forecasting with artificial neural networks to determine the optimal sizing of solar and wind systems. This method ensured reliable and cost-effective energy supply for remote and off-grid areas, highlighting the value of hybrid modeling in diverse contexts.

In renewable energy configurations, combining algorithms, such as neural networks, with physical models has proven effective for optimizing energy distribution and mitigating intermittency. For instance, Tian et al. [121] proposed a dual-driven modeling approach to optimize the operation of integrated electricity–heat systems. By incorporating both data-driven and physics-based modeling, the hybrid system achieved enhanced performance in energy flow calculations, which is vital for real-time control of power grids with renewable energy source.

3.3.4. Smart Grid Operations

The hybrid modeling approach is transforming smart grid operations by providing enhanced capabilities for real-time monitoring, fault detection, and dynamic optimization. Smart grids, characterized by their decentralized and dynamic nature, require robust models that can integrate diverse data streams and operate under varying conditions. Hybrid models address these needs by combining the strengths of data-driven techniques, such as machine learning, with physics-based models grounded in the laws of energy systems.

For instance, Alvarez et al. [122] explores the integration of optimization and machine learning techniques in data-driven models supports optimal operation and management of smart grids. These models help achieve near-optimal operation under normal conditions and ensure reliability during disruptive events.

A hybrid Digital Twin model, which combines a data-driven machine learning subsystem with a discrete deterministic one, enables near real-time fault identification. This allows for early detection of transient disturbances and provides preventive actuation possibilities. For example, Gui et al. [123] develops a hybrid Digital Twin model, which combines a data-driven machine learning subsystem with a discrete deterministic one, enables near real-time fault identification. This allows for early detection of transient disturbances and provides preventive actuation possibilities

Another application of hybrid models lies in energy storage system management and optimization. Accurate modeling of energy storage behavior is essential for grid stability, particularly as renewables such as solar and wind introduce variability in energy supply. For example, Chen et al. [124] proposes a meta rule-based energy management strategy for battery/supercapacitor hybrid, aiming to optimize the efficiency and battery life of the hybrid energy storage system in electric vehicles.

4. Future Avenues for Integrating AI-Enabled Techniques in Multidimensional Energy System Modeling

With the growing complexity of renewable energy integration, decentralized grids, and dynamic market demands, advanced AI-driven modeling techniques offer unparalleled capabilities to tackle multidimensional challenges. By enhancing data analytics, predictive modeling, and adaptive optimization, AI improves forecasting accuracy, real-time decision-making, and system resilience. This section delves into the future directions for AI-enabled techniques in energy system modeling (

Table 4. Main topic for hybrid energy system modeling.

Topic	Description	Key Approaches
Integration and Optimization for Renewable Energy Systems	Dynamically optimizing renewable energy production, distribution, and storage strategies	Machine learning algorithms and energy flow/optimization models
Real-Time Optimization and Predictive Maintenance through Digital Twins	Real-time monitoring, simulation, and predictive maintenance of energy systems	Machine learning algorithms and energy simulation model
Advanced Demand-Side Management for Optimal Energy Use	Managing consumer energy use, optimizing grid stability, and enhancing energy efficiency	Machine learning algorithms and energy simulation/optimization model
Hybrid Simulation of Energy Markets and Business Behavior	Predicting market behavior, business strategies, and regulatory impacts	Machine learning algorithms, mechanism-driven models, and behavioral economics

), including the following:

• Integration and optimization for renewable energy systems;
• Real-time optimization and predictive maintenance through digital twins;
• Advanced demand-side management for optimal energy use;
• Hybrid simulation of energy markets and business behavior.

4.1. Integration and Optimization of Renewable Energy Systems

Hybrid modeling significantly enhances the optimization of renewable energy systems by combining the exploratory capabilities of data-driven models with the reliability and interpretability of mechanism-driven models. In renewable energy systems that integrate solar, wind, and energy storage, this approach dynamically optimizes energy production, distribution, and storage strategies. Mechanism-driven models provide detailed simulations of energy flows and system dynamics, offering insights into the physical behavior of the system under varying conditions. Simultaneously, data-driven optimization algorithms refine control strategies in real time, allowing systems to adapt to fluctuations in energy demand or renewable energy generation. By accurately forecasting renewable output and optimizing the use of storage resources, hybrid models help reduce operational costs and environmental impact [125].

Additionally, data-driven techniques, such as evolutionary algorithms—including genetic algorithms, particle swarm optimization, and differential evolution—excel in solving complex, multi-objective optimization problems. These methods explore vast solution spaces to determine the optimal configuration of renewable energy systems, such as the optimal sizing and placement of solar panels, wind turbines, and batteries. When these approaches are combined with physics-based models, they ensure that the proposed solutions meet critical physical and operational constraints. For instance, data-driven models might suggest energy-efficient strategies, while mechanism-driven models validate their feasibility under real-world conditions, such as varying weather patterns or grid stability requirements [126,127]. This hybrid approach provides a balance between theoretical optimization and practical implementation, enhancing system reliability, adaptability, and performance.

4.2. Real-Time Optimization and Predictive Maintenance Through Digital Twins

Digital twin technology is transforming energy system management by providing highly detailed virtual replicas of physical assets, enabling real-time monitoring, simulation, and optimization. These virtual models, driven by a continuous stream of real-time data, capture the dynamic behavior of energy systems, offering critical insights into performance, efficiency, and potential failures. By integrating advanced modeling techniques, digital twins can simulate complex system interactions and predict future states with high accuracy, ensuring optimal performance under varying conditions [128,129].

A unique strength of digital twins lies in their ability to integrate data-driven models, such as machine learning algorithms, with physics-based models. Data-driven approaches excel at analyzing large volumes of historical and real-time data, identifying patterns, and predicting anomalies. These algorithms enable the optimization of control strategies by learning from past system behavior and dynamically adapting to new conditions. Concurrently, physics-based models provide a deep understanding of system dynamics, capturing fundamental physical relationships and ensuring that optimizations remain within critical operational and safety limits. This hybrid approach allows digital twins to deliver highly accurate, robust, and actionable insights, making them an indispensable tool for complex energy systems [130].

One of the most impactful applications of digital twin technology is in predictive maintenance. By continuously monitoring the health and performance of critical energy assets—such as turbines, transformers, and battery storage systems—digital twins can identify potential failures before they escalate [131]. Data-driven algorithms detect subtle anomalies and early warning signs of wear or malfunction, while physics-based models simulate the progression of these issues under various operating scenarios. This dual capability enables proactive maintenance, allowing operators to address potential problems before they result in costly downtime or damage. In turn, this reduces maintenance costs, extends the lifespan of infrastructure, and enhances system reliability [132,133].

4.3. Advanced Demand-Side Management for Optimal Energy Use

Hybrid modeling approaches present a transformative opportunity for optimizing demand-side management strategies, aimed at optimizing energy consumption patterns to improve grid stability, enhance energy efficiency, and reduce operational costs. By integrating data-driven machine learning techniques with physics-based simulation models, hybrid frameworks enable a more dynamic, accurate, and efficient management of energy consumption at the consumer level. Demand-side management strategies such as load shifting, demand response, and peak shaving are crucial for balancing supply and demand, reducing grid stress, and enhancing the economic viability of energy systems [134]. Hybrid models can improve the prediction of consumer behavior, load profiles, and responsiveness to price signals by combining real-time data from smart meters and IoT sensors with historical usage patterns and physical models of the grid infrastructure.

Flexible load management is a key application of advanced demand-side management strategies, particularly in smart grids that integrate renewable energy sources and electric vehicles. As renewable energy generation is inherently variable, advanced demand-side management strategies dynamically adjust energy demand to align with supply, optimizing the utilization of renewable energy. For example, during periods of high solar or wind generation, demand-side management strategies can shift the operation of flexible loads—such as EV charging, industrial processes, and smart appliances—to take advantage of abundant renewable energy. Conversely, during low renewable generation periods, these loads can be reduced or shifted to avoid overloading the grid [135,136].

The integration of EVs into smart grids further amplifies the potential of flexible load management. EVs can act as both consumers and suppliers of energy, thanks to vehicle-to-grid technology. Advanced demand-side management systems can optimize EV charging schedules, ensuring that vehicles are charged during off-peak hours or when renewable energy supply is high. Additionally, during peak demand periods, EVs can discharge energy back into the grid, enhancing grid stability and reducing reliance on conventional power plants [137]. By leveraging real-time data and predictive analytics, demand-side management strategies ensure that the grid operates efficiently while minimizing carbon emissions and energy costs [138].

Building energy management systems are another critical application of advanced demand-side management. Advanced demand-side management enables building energy management systems to participate in demand response programs, adjusting energy consumption in real time based on grid conditions or market signals. For instance, during peak demand periods, building energy management systems can reduce HVAC usage, dim lighting, or defer non-essential energy-consuming activities, thereby contributing to grid stability and reducing energy costs [139]. Moreover, building energy management systems enhance the integration of on-site renewable energy systems, such as rooftop solar panels and energy storage systems [140]. It can optimize the charging and discharging of batteries, ensuring that stored energy is used efficiently during peak demand or grid outages.

4.4. Hybrid Simulation of Energy Markets and Business Behavior

The future of hybrid modeling approaches in energy markets and business behavior holds several exciting avenues for development. Hybrid modeling, which combines data-driven models with mechanism-driven models, help assess the impact of policy changes, price fluctuations, and consumer behavior on energy demand and supply. These simulations provide insights into market dynamics, optimize resource allocation, and support decision-making for sustainable energy strategies.

One promising area for hybrid modeling is to enhance market predictions and business strategies. By using machine learning to analyze large datasets and identify complex patterns, and then integrating these insights with mechanism-driven models that simulate energy flows, supply-demand dynamics, and market behavior, hybrid models can improve decision-making processes [141,142]. These models can be used to simulate energy trading, pricing strategies, and market disruptions, providing businesses with predictive tools for optimizing operations and responding to market changes. Future developments could focus on creating more robust, real-time decision support systems for market participants, powered by the integration of data-driven and mechanism-based modeling approaches.

Another avenue is improving the modeling of market design and regulation. Energy markets are heavily influenced by policy frameworks, market structures, and regulatory interventions. Hybrid modeling can play a crucial role in simulating different regulatory scenarios, understanding their impact on market stability, and assessing the economic viability of new business models [143]. By combining the data-driven understanding of market participant behavior with the mechanism-driven insights into energy system operations, hybrid models can simulate the effects of policy changes, such as carbon pricing or renewable energy incentives, on market dynamics and business profitability [144]. Future research could focus on refining these models to create more realistic simulations of market behavior under varying regulatory conditions, which could inform policy development and strategic business decisions.

The integration of behavioral economics into hybrid models is another promising direction. Business behavior in energy markets is not solely driven by rational decision-making; it is influenced by factors such as risk perception, uncertainty, and psychological biases. Hybrid models that combine data-driven behavioral insights with the formal, mechanism-driven representation of market systems can provide a more holistic view of how businesses make decisions. By modeling not only the economic fundamentals but also the psychological factors that drive decision-making in energy markets, hybrid models can offer deeper insights into market behavior, investment decisions, and the response to regulatory changes [145,146,147]. This could help businesses better anticipate and adapt to changing market conditions.

4.5. Key Challenges

4.5.1. Data Quality and Availability

Hybrid modeling approaches often rely heavily on integrating large datasets from both data-driven and mechanism-driven models. High-quality, consistent, and comprehensive data are essential for accurate predictions and simulations. However, in energy systems, obtaining such data can be challenging due to gaps in historical records, variations in data resolution, and issues with data accessibility across different regions and systems. Inadequate or poor-quality data can lead to biased model results, unreliable conclusions, and reduced accuracy. Additionally, when data are unavailable for certain parameters, assumptions must be made, which can introduce uncertainty into the model outputs [148].

To address challenges related to data quality and availability, several strategies can be employed. First, collaboration among stakeholders—including governments, energy companies, and research institutions—can improve data collection efforts, ensuring that data is consistently gathered and updated across regions. Leveraging alternative data sources, such as satellite imagery or real-time sensor networks, can help fill gaps in traditional datasets. Furthermore, data preprocessing techniques such as cleaning, imputation, and anomaly detection can improve the quality of available data. Data fusion methods, which combine information from multiple sources, can also help mitigate issues arising from incomplete datasets, enhancing the robustness of the models.

4.5.2. Computational Complexity

Hybrid models that combine both data-driven and mechanism-driven approaches tend to have high computational demands. Data-driven models often require processing vast amounts of historical data, while mechanism-driven models involve solving complex mathematical equations based on physical laws. The combination of these two approaches increases the computational complexity, making simulations more time-consuming and resource-intensive [149]. This can limit the practical applicability of these models, especially for large-scale energy systems, which require real-time or near-real-time analysis.

To manage computational complexity, several strategies can be employed. High-performance computing resources, such as cloud computing or parallel processing, can speed up the simulations, enabling faster analysis of large, complex energy systems. Model simplification is another approach; less critical components of the system can be approximated or excluded without compromising the accuracy of the results. Additionally, surrogate models—simplified representations of the more computationally intensive models—can be used for specific tasks such as optimization or sensitivity analysis. Machine learning techniques can also be leveraged to identify patterns in data, reducing the need for exhaustive simulations while maintaining predictive accuracy. These strategies can help improve the efficiency of hybrid models, ensuring that they remain practical for large-scale, real-time applications.

4.5.3. Model Compatibility and Integration

The integration of data-driven and mechanism-driven models presents significant challenges in terms of compatibility. Data-driven models typically focus on identifying patterns and correlations from historical data, while mechanism-driven models are based on physical laws or theoretical frameworks. These two approaches often operate at different scales, use different assumptions, and generate outputs that may not align seamlessly. Integrating them requires careful alignment of these models’ structures, ensuring that their interactions are coherent and that they complement each other effectively. Mismatched assumptions or incompatible formats can lead to inconsistent or unreliable model outputs [150].

To overcome these challenges, a systematic approach is required, including the use of standardized modeling frameworks and common interfaces that facilitate interaction between data-driven and mechanism-driven models. Developing hybrid modeling platforms that can accommodate both types of models is another solution. Additionally, sensitivity analysis and validation techniques should be employed to ensure the integrated models produce consistent and reliable results. Cross-validation between the models is essential to refine the hybrid framework and ensure that the integration process does not introduce significant inconsistencies. Effective communication and collaboration between data scientists and energy system experts are also critical for ensuring that model integration is both technically feasible and scientifically sound.

4.5.4. Interdisciplinary Expertise

Hybrid modeling approaches require collaboration across multiple disciplines, including energy systems engineering, data science, machine learning, and mathematics. This interdisciplinary nature can present challenges, as each field has its own methodologies, terminologies, and assumptions. Bridging the gap between these fields is essential for creating models that are both scientifically robust and computationally efficient. However, differences in perspectives or communication barriers can slow down progress and hinder the integration of diverse expertise [151].

To address this, fostering collaborative environments is key. One solution is to promote joint education and training programs that equip professionals with cross-disciplinary knowledge. This can be achieved through workshops, cross-disciplinary conferences, or collaborative research projects. Establishing common frameworks or languages for communication can also help align experts from different fields, ensuring that all team members have a shared understanding of the models’ assumptions and goals. Moreover, having dedicated project managers with expertise in both the technical and energy system aspects can help ensure that the project stays on track. By encouraging a culture of shared problem-solving and knowledge exchange, teams can effectively overcome the challenges posed by interdisciplinary collaboration.

5. Conclusions

The integration of AI into energy system modeling represents a transformative step toward more efficient, sustainable, and adaptive energy management. As global efforts to achieve carbon neutrality intensify, the role of AI-driven models becomes indispensable for optimizing energy systems to meet the challenges posed by increasing complexity, renewable energy integration, and real-time decision-making needs. This review has provided a comprehensive analysis of the three primary AI-enabled modeling approaches: data-driven, mechanism-driven, and hybrid models, along with a discussion of their unique characteristics, practical applications, and associated challenges.

The hybrid modeling approach is poised to become a key AI-enabled solution for addressing real-world challenges by combining the strengths of both data-driven and mechanism-driven models. Data-driven models, powered by advanced machine learning, excel at pattern recognition and prediction from large datasets, making them ideal for tasks such as demand forecasting, anomaly detection, and energy efficiency analysis. However, their dependence on high-quality data and their “black-box” nature limit their interpretability, reducing their effectiveness in critical decision-making. Conversely, mechanism-driven models, based on physical laws and domain knowledge, provide transparent, theory-based insights into system dynamics and energy flow. While they offer interpretability, they require significant computational resources and struggle with adapting to rapidly changing conditions. By merging predictive power with theoretical rigor, hybrid models enhance precision, adaptability, and interpretability in energy system modeling, effectively addressing variability and uncertainty.

Looking forward, the future of AI-enabled energy system modeling holds significant promise, with hybrid models set to play a central role in optimizing renewable energy integration, real-time optimization and predictive maintenance through digital twins, advanced demand-side management, and hybrid simulations of energy markets and business behavior. These models are expected to revolutionize energy systems by combining the strengths of both data-driven and mechanism-driven approaches. However, challenges such as data availability, computational efficiency, and the need for interdisciplinary collaboration persist. To unlock the full potential of hybrid modeling, it will be essential to develop efficient algorithms; foster improved data-sharing mechanisms; and strengthen collaboration among AI researchers, energy system engineers, and policymakers. Overcoming these barriers will pave the way for more adaptive, accurate, and sustainable energy solutions.