Residential energy consumption plays a crucial role in the overall energy landscape, and it accounts for a significant portion of global energy demand [1]. This sector encompasses various energy-intensive activities, including space heating, water heating, lighting, and the operation of electric appliances and devices in millions of households worldwide [2]. The global population continues to grow [3], which leads to an increase in the number of residential units, and subsequently to higher energy requirements in meeting basic living needs. Additionally, rapid urbanization [4] and improved living standards in many regions have resulted in increased energy consumption per household. The sector’s energy demand is further influenced by factors such as population density, climate conditions, building characteristics, and socio-economic factors [5]. It is therefore crucial to understand and address the complexities of energy consumption to transition to a low-carbon future.

Energy fuels economic growth by powering industries, transportation, and essential services. As societies continuously evolve and technology becomes more pervasive, energy needs continue to increase. Nevertheless, the world is grappling with the adverse consequences of excessive energy use [6], such as greenhouse gas emissions, climate change, and resource depletion. It is therefore imperative to recognize that energy use is inextricably linked to global challenges [7], which makes it vital to explore diverse sustainable practices to find those that balance economic growth with environmental responsibility.

Improving energy efficiency and promoting sustainable energy practices have emerged as critical global priorities to reduce energy consumption in the residential sector [8]. The understanding of residential energy consumption patterns is a crucial step to design effective policies [9], implement targeted interventions, develop sustainable energy systems [10], and reduce our environmental footprint. Mitigating energy consumption in the residential sector requires a multi-faceted approach. Key strategies include the promotion of energy-efficient technologies, behavioral changes, and policy interventions. Adopting energy-efficient appliances, optimizing heating and cooling systems, implementing better insulation, and utilizing renewable energy sources are all effective measures through which to reduce energy consumption.

Researchers studied many tools and technologies that facilitate the reduction in energy consumption in households. For example, smart meters can, depending on the technology and legal situation, enable real-time energy monitoring [11], which empowers homeowners to track and improve their consumption. Home energy management systems [12] allow for the automatic control and adjustment of energy demand and price signals based on electrical equipment. In addition, the development of energy storage and distributed energy resources offer opportunities to efficiently integrate renewable energy sources in residential systems [13].

Modeling plays an important role in shaping our understanding of energy use patterns and potential pathways toward sustainability [14]. It also provides a systematic framework through which to analyze the complex interplay of the various factors influencing residential energy use. The developed mathematical and computational models [15] that simulate energy use offer a systematic and structured approach in studying this field.

2. Modeling Techniques in Residential Energy Consumption

2.1. Causal Modeling

Causal models based on causal interference and analysis are powerful analytical tools that have been mainly used to identify and understand the cause and the effect relationships between the system variables. Unlike correlation, which only shows the association between the variables, causal models aim to determine the underlying mechanisms that lead to certain outcomes. In the context of modeling energy consumption in the residential sector, causal models can discover the complex interactions between various factors that influence household energy usage. Causal models, including structural equation modeling (SEM) [16], Bayesian networks [17], time series causal models [18], quasi-experimental designs [19], and randomized controlled trials (RCTs) [20], have emerged as powerful tools through which to understand the complex relationships and interdependencies influencing energy consumption in households. Causal modeling in residential energy consumption has expanded beyond traditional techniques, with structural causal models (SCMs) [21] gaining traction. SCMs allow researchers to capture both the direct and indirect causal relationships among variables, providing a more comprehensive understanding of energy usage drivers. Additionally, the potential outcome framework (POF) [22], commonly used in causal inference, is employed to estimate causal effects by comparing the observed and the counterfactual scenarios. SCMs and the POF serve as solid foundations as they enable the consistent representation of prior causal knowledge, assumptions, and estimates. The POF takes potential outcomes as a starting point and relates them to observed outcomes, while SCMs define a model based on observed outcomes from which potential outcomes can be derived. Causal models need to fulfill seven essential tasks [23] to be valuable tools for causal inference. To simulate the effects on the behavior of energy consumption in the residential sector through causal models, researchers must carefully select and collect relevant data about household energy consumption, demographic information, weather data, and details on energy-efficient technologies. Furthermore, survey data can also provide valuable insights into behavioral factors influencing energy usage. Advanced statistical software and programming languages (e.g., R and Python) are indispensable tools for data analysis and for developing causal models. When selecting libraries to build causal models, several implementation aspects should be considered, such as the license type, programming language, documentation quality, and availability of support channels. Libraries that offer support tools for creating, modifying, and converting causal diagrams enhance the usability of causal models and their interpretability. Several libraries implementing previous aspects have been studied, including DAGitty [24], DoWhy [25], Causal Graphical Models [26], Causality [27], and Causal Inference [28]. Research exploring the application of SCMs and the potential outcome framework to residential energy consumption is growing. For instance, the study of [29] utilized SCMs to analyze the causal relationships between the characteristics of the buildings, occupancy patterns, and energy use in residential buildings. Their findings emphasized the substantial impact of occupant behavior on energy consumption, uncovering valuable insights for energy efficiency initiatives. Another study [30] employed the directed acyclic graph (DAG) when randomized controlled trials (RCTs) were not feasible to assess the causal effects of household energy savings. Their research demonstrated that DAGs lead to a better understanding of the processes underlying intervention programs.

2.2. Modeling of Energy Systems in Buildings

Different models that provide a comprehensive understanding of energy use across different scales are examined. The examination covers a wide range of systems, from individual buildings to complex, large-scale energy supply systems. Generally small-scale system models tend to be far more detailed than large-scale system models, but that strongly depends on their intended use. From a modeling perspective, energy system models consist of multiple interconnected models that work within a unified framework. The level of detail in these models is different as it depends on the scale of the representation, from complex representations of valves and pipes in a building to simplified representations of building blocks. The accuracy and the quality of the obtained results are greatly influenced by modeling methodology and the available computation time. Table 1 provides a brief overview of some of the identified models, but the list is not exhaustive. The intention is to offer readers a glimpse into the possibilities and scales of energy system models. Ten different models have been examined, each with unique characteristics and applications. The above modeling tools do not only contribute to the comprehensive understanding of energy consumption patterns, but can also analyze the impact of various interventions in residential buildings. The utilization of these models can offer valuable insights into the patterns and origins of energy consumption, the optimization of energy usage, and can help to inform the decision-making process related to energy planning and management.

2.3. Modeling the Linkage of Mobility with Residential Energy

The seamless interaction between mobility and residential energy consumption plays a pivotal role in shaping sustainable urban living. As individuals commute to work, access essential services, and partake in recreational activities, their transportation choices directly impact their household’s energy usage. Electric vehicles (EVs) and the integration of smart mobility solutions introduce new dynamics to the energy landscape. With charging points being set up in homes, EVs now directly link the energy consumption for mobility with the household energy consumption. The ever-increasing importance of sustainable urban living and energy-efficient mobility has paved the way for innovative traffic simulation models that align with the residential sector’s needs. Within this context, four influential simulation models, which are examined in this text—namely SUMO, MATSim, VISSIM, and PRIMES-TREMOVE—have emerged as powerful tools through which to analyze transportation dynamics (Table 2). With a focus on energy-conscious mobility and enhanced accessibility, these models offer valuable insights to the interplay between residential mobility patterns and energy consumption. These methodologies have many proven success stories, but they have a fundamental limitation in capturing social behavior, which influences the decision of using specific transport modes. Thus, social behavior affects (i.e., time cost, comfort, monetary cost, or environmentally friendly awareness) are not included in the models. Actual platforms for road simulation do not cover these needs, either due to the impossibility to parameterize the initial system configuration according to social variables, or due to the distribution of such modules as additional commercial packages.

2.4. Modeling Approaches for Enhancing Energy Efficiency in Buildings

Improving energy efficiency in buildings is a pivotal issue for sustainable development. Modeling energy efficiency involves the use of various software tools and methodologies to simulate and analyze the energy performance of buildings. Some of the key measures that current tools consider are shown in Table 3.

2.7. Modeling Generation Technologies

The increasing prominence of decentralized generation capacities has elevated the significance of accurately simulating these technologies in the household sector. The study focuses on modeling approaches tailored to generation technologies that are relevant to residential settings, including PV generation (rooftop PV, facade PV, and bifacial PV), small-scale wind turbines, CHP technologies (gas-powered CHP, hydrogen-powered, and CHP fuel cells), and combustion engines. Each technology exhibits distinct characteristics that require unique modeling techniques to ensure accurate representation and performance simulation.

2.8. Modeling Business Models in the Field of Electrical Consumption on a Household Level

The European Union’s energy landscape is experiencing a transformative shift with energy consumers playing a more active role in the energy system. Decentralized generation capacities and controllable flexible loads have unlocked opportunities for consumers to interact with the energy market in innovative ways, triggering the emergence of new business models. Table 4 introduces the concept of business models in the household sector, setting the stage for the exploration of various models that offer consumers greater control over their energy consumption and costs. Energy efficiency modeling tools in buildings encompass diverse categories, each tailored to address specific aspects of energy consumption. Whole-building simulation tools, like EnergyPlus [45] and DesignBuilder [46], offer dynamic simulations of overall building energy performance, thus allowing for the comprehensive analysis of heating, cooling, ventilation, lighting, and other systems. Energy labeling models [47] incorporate energy labels and certificates to assess and rate the buildings based on their energy performance and compliance with specific standards. Retrofit assessment models [48] focus on assessing the impact of renovation measures on building energy efficiency, thus aiding in identifying cost-effective retrofit strategies. While modeling tools have advanced significantly, they do have limitations such as data accuracy and integration complexity. Accurate input data [49], such as occupancy patterns [50] and weather conditions, are crucial for reliable results, but obtaining them is challenging. Moreover, the interactions between building systems might not be fully captured and lead to potential inaccuracies [51]. Nevertheless, these modeling tools have demonstrated successes in performance prediction, cost-effectiveness, and policy support. They enabled informed decision making during the design phase leading to cost savings by identifying energy-efficient measures. Furthermore, many models support policy makers in developing energy efficiency regulations and standards.

2.5. Modeling Energy Management Systems (EMS)

Energy management systems (EMS) utilize measured data, forecasts, and self-learning algorithms to optimize energy consumption by shifting flexible loads to times when it is more economic, ecological, or convenient. There exists a multitude of different EMS options tailored with different consumer ranges, which can be classified into clusters, namely (1) open-source EMS, (2) research EMS, and (3) commercial EMS. Modeling energy management systems involves considering their specific functionalities, integration capabilities, and potential applications in achieving energy sufficiency within the residential sector. The above table demonstrates the diverse range of EMS options available and the benefits they offer in terms of optimizing energy consumption, promoting energy efficiency, and fostering user engagement in energy conservation.

2.6. Modeling Energy Storage

Energy storage provides energy systems with the necessary flexibility to mitigate the effects of an increasing amount of variable renewable energy. Effective energy storage models can help optimize energy usage, improve system resilience, and contribute to a more sustainable and efficient energy system design. This discussiontudy focuses on the mathematical representation of the storage system itself and the models describing its control strategy and interactions with other systems. The storage systems considered in this textstudy are clustered according to the technology used. The relevant clusters are as follows:

• Electro-Chemical Storages

• ◦

  Classical Batteries

  ▪

  Li-Ion Technology

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  Nickel Cadmium Technology

• ▪

  Nickel Metal Hydride Technology

  ▪

  Zinc–Air Technology

  ▪

  Sodium Sulfur Technology

  ▪

  Sodium Nickel Chloride Technology

  ▪

  Lead Acid Technology

  ◦

  Flow Batteries

  ▪

  Vanadium Redox Flow Technology

• ▪

  Hybrid Flow Technology

• Chemical Storages

  ◦

  Hydrogen

  ◦

  Synthetic natural gas

  ◦

  Biomethanation

• Mechanical

  ◦

  Flywheel

  ◦

  Pressure

• Electrical

  ◦

  Supercapacitor

  ◦

  Superconducting Magnetic

• Thermal

  ◦

  Sensible Heat

  ◦

  Latent Heat

  ◦

  Thermo-Chemical

2.9. Urban Energy Modeling and Microclimates

To achieve sustainability in a greater scale however, urban energy modeling techniques are being employed that consider the residential sector a pivotal factor in contributing to the urban energy canvas. The energy requirements of residential buildings such as heating, cooling, lighting, and everyday appliances reflect the city’s energy footprint. Conversely, the density, the infrastructure, and the design of the urban environment exert a tangible influence on residential energy use. Currently, urban energy modeling is progressing with improved data availability and more sophisticated simulation techniques that encompass diverse factors such as transportation, infrastructure, and land use. Recent research in urban modeling spheres underscores the significance of data acquisition techniques in refining urban building energy models. The study of ^[58][133] aggregated and analyzed data from diverse sources to gain models with the appropriate granularity in order to capture the nuances of energy consumption in different urban zones. Upon this, another study ^[59][134] explored the pertinent questions that drive the evolution of urban energy modeling. Their inquiries ranged from the impact of urban form in energy demand to the integration of renewable energy sources within urban contexts. Urban microclimates indeed correlate with both urban and residential modeling. Microclimates are influenced by factors such as building density, vegetation, and surface materials. It impacts energy demand, heat distribution, and cooling strategies in both contexts. The integration of microclimate data enhances the accuracy of energy models for both areas. The study of ^[60][135] investigated the relevant techniques in urban thermal and wind environments, concluding that current techniques cannot pave the way for accurate strategies; furthermore, it suggested that future modeling assessments should include urban typologies and data-driven approaches for more accurate decisions. Another study ^[61][136] focused on the recent advancements of urban microclimates on urban wind and thermal environments that were found (although field measurements were the most necessary for this type of assessment, the techniques used to achieve the desired accuracy in results were missing).