Modeling Approaches for Residential Energy Consumption

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1		Thomas Nacht	--	3839	2023-09-26 10:26:10	\|
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This entry is adapted from the peer-reviewed paper 10.3390/cli11090184

The residential sector is one of the most energy-intensive sectors and plays an important role in shaping future energy consumption. In this context, modeling has been extensively employed to identify relative key drivers, and to evaluate the impact of different strategies to reduce energy consumption and emissions.

building energy consumption electricity demand energy efficiency energy management modeling approaches energy systems

1. Introduction

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.

Table 1. Comparison of models for Energy Systems at Different Scales.

Load Profile Generator (Building Scale)	This modeling tool focuses on individual households and performs a comprehensive simulation of household behavior to generate load curves ^[31].
Energy Plus (Building Scale)	EnergyPlus is an energy analysis and thermal load simulation program that calculates building’s geometry, materials, and systems ^[32].
EnergyPlan (Large scale energy systems)	EnergyPlan is a computer model for energy system analysis that enables the design of national energy planning strategies by analyzing the consequences of different energy systems and investments at hourly intervals ^[33].
MATLAB & Simulink (General Modeling Environment)	MATLAB & Simulink provide an integrated platform for data analytics and model-based design, allowing for the creation of predictive models for cost minimization ^[34].
Simscape Electrical Specialized Power Systems (Different scales of Energy Systems)	This software, part of the Physical Modelling product family, allows for the rapid simulation of power systems with interactions across various disciplines, including electrical, mechanical, thermal, and control systems ^[35].
TRaNsient SYstem Simulation program (Different Scales of Energy Systems)	TRNSYS is a flexible software environment that simulates the behavior of transient systems. It comprises an engine for system processing and an extensive library of components that model various aspects of the system ^[36].
RC-Building Simulator (Building Scale)	Based on the resistor capacitor (RC) model, this physics-based simulation tool accurately captures the thermal behavior of buildings using an electrical analogy ^[37].
ESP-r (Building Scale)	ESP-r is a building energy simulation program that allows for the integrated modeling of energy performance, through which it considers heat, air, moisture, light, and electrical power flows ^[38].
IDA ICE (Building Scale)	IDA Indoor Climate and Energy (ICE) is a program used to study the indoor climate of individual zones within a building, and it is used to analyze energy consumption for the entire building ^[39].
Modelica Building Systems (Different Scales of Energy Systems)	The Modelica Building Systems library enables dynamic simulations of energy behavior in single rooms, buildings, and districts. It accounts for the energy balance of building envelopes and can incorporate energy plant systems, such as solar heating systems ^[40].

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.

Table 2. Noteworthy projects and models in traffic simulation and transportation modeling.

SUMO ^[41]	An open source, microscopic, and multi-modal traffic simulation package designed to handle large road networks and serve as a test bed for traffic research algorithms and models. While it allows interoperability with external applications during runtime, it requires an explicit definition of route steps for each citizen.
MATsim ^[42]	An agent-based transportation simulation framework capable of simulating large-scale scenarios. Originally focused on private car traffic, it was later expanded to include various public transportation modes, pedestrians, and cyclists.
VISSIM ^[43]	A microscopic simulation model based on the Wiedemann model, enabling highly accurate traffic simulations for functionally classified roadways and public transportation operations.
PRIMES_TREMOVE ^[44]	An economic model that combines microeconomic behavior with a detailed representation of transport technologies. It includes a transport demand module based on decision trees, and it is used to emulate consumer profile decision-making processes.

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.

Table 3. Key measures considered in energy efficiency modeling tools.

Building Envelope	Evaluates the building envelope, including walls, roofs, and floors, to assess the levels of insulation and thermal performance. Proper insulation helps in reducing heat transfer and energy loss.
Windows and Glazing	These tools analyze the type of windows and glazing used in buildings, whereby factors like the u-value, solar heat gain coefficient (SHGC), and shading devices are considered. Window replacements and glazing improvements can significantly impact the overall energy efficiency.
HVAC Systems	These models analyze heating, ventilation, and air conditioning (HVAC) systems to assess their energy consumption and efficiency. Evaluating HVAC performance helps identify the opportunities for energy savings and optimization.
Lighting	Tools that analyze lighting systems, including light fixtures and controls, to evaluate their energy consumption and potential for efficiency improvements.
Occupancy & Scheduling	Some of these tools allow for the integration of occupant behavior and schedules to simulate real-world usage patterns, which can influence energy consumption.
Appliances & Equipment	Energy efficiency models may incorporate the energy consumption of various appliances and equipment, such as refrigerators, computers, and other electronic devices.
Renewable Energy Integration	Some advanced tools consider the integration of renewable energy sources like solar panels or wind turbines to assess the potential for on-site energy generation and its impact on overall energy efficiency.
Building Orientation	Building orientation is on how the amount of sunlight received affects the energy of the building, which can impact heating and cooling loads. These modeling tools consider the orientation of the building to optimize energy efficiency.
Energy Labels & Certificates	Certain tools incorporate energy labels and certification systems to assess and rate buildings based on their energy performance and compliance with specific standards.
Energy Codes & Regulations	Energy efficiency models can be aligned with building codes and regulations to ensure compliance and identify relevant areas for further improvements.
Retrofit Assessment models	These specialized models play a crucial role in guiding retrofit decisions, thus enabling informed choices that result in reduced environmental impact and energy savings.

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 discussion 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 text are clustered according to the technology used. The relevant clusters are as follows:

Electro-Chemical Storages

◦

Classical Batteries

▪

Li-Ion Technology

▪

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

2.7. 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.

Table 4. Overview of business models available to household energy consumers.

Energy as a Service (EaaS) ^[52]	EaaS is an innovative business model that extends beyond the traditional supply of electricity. Energy service providers (ESPs) offer various energy-related services to consumers, enabling them to optimize their energy consumption and reduce costs. These services include energy consulting, finance schemes for assets, energy management technologies, and assistance with tariff changes. By leveraging EaaS, consumers can actively participate in optimizing their energy consumption and accessing tailored services from ESPs.
Peer-to-Peer Electricity Trading (P2P) ^[53]	Peer-to-peer electricity trading allows consumers to directly exchange electricity surplus with each other, bypassing traditional energy suppliers. Different approaches, such as direct power purchase agreements or interconnected web-based platforms, facilitate P2P trading. Consumers become members of the platform, either through a subscription fee or other mechanisms, and engage in direct electricity trading. Although P2P trading is not yet implemented in all EU countries, recent directives have mandated the introduction of P2P and direct electricity selling, paving the way for its future adoption.
Aggregators ^[54]	Aggregators are service providers that represent a group of agents, such as consumers, producers, and prosumers, as a single entity in the system. Aggregators enable their agents to participate in specific market segments by reaching the required thresholds. These market segments include wholesale electricity markets and various power control mechanisms. Aggregators work in conjunction with virtual power plants (VPPs), which aggregate dispersed energy sources and flexibilities. Through information technology, VPPs optimize the use of assets based on real-time data, market conditions, and consumption trends, thus allowing aggregators to share in their agents’ profits.
Community-Ownership Models ^[55]	Community-ownership models aim to facilitate the collective ownership, management, and utilization of generation capacities and energy-related assets. These models address barriers to individual investments in renewable energy technologies by allowing individuals to own shares in community-owned assets. There are different types of community-ownership models, including economic benefit sharing models, collective self-consumption schemes, and energy communities. These models provide financial and environmental benefits to participants and can be organized in various ways, such as cooperatives, partnerships, non-profit organizations, or community trusts.
Pay-as-You-Go Model ^[56]	The pay-as-you-go (PAYG) model offers a new approach through which to address energy poverty and provide access to electricity in both well-connected regions and remote areas with limited or no grid access. PAYG requires customers to pay upfront, giving them greater control over their electricity bills and consumption. This model combines decentralized and isolated energy generation from renewable sources with upfront payments, allowing users to gradually obtain ownership of devices through micro payments. PAYG models can be implemented at the household level, in a broader community, or on a neighborhood scale.
Conventional Energy Supply Models ^[57]	Conventional energy supply models vary across countries and energy suppliers. These models typically involve periodically measured consumption values, including energy consumption-related values, power-related values, and fixed costs. Energy bills often comprise energy tariffs, grid tariffs, taxes, and fees, with some components being unaffected by energy consumption or power usage.

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] 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] 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] 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] 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).

References

del Pablo-Romero, M.P.; Pozo-Barajas, R.; Yñiguez, R. Global changes in residential energy consumption. Energy Policy 2017, 101, 342–352.
Vassileva, I.; Campillo, J. Increasing energy efficiency in low-income households through targeting awareness and behavioral change. Renew. Energy 2014, 67, 59–63.
Holdren, J.P. Population and the energy problem. Popul. Environ. 1991, 12, 231–255.
Zhang, X.Q. The trends, promises and challenges of urbanisation in the world. Habitat Int. 2016, 54, 241–252.
Guo, R.; Cheng, L.; Li, J.; Hahn, P.R.; Liu, H. A Survey of Learning Causality with Data. ACM Comput. Surv. 2020, 53, 1–37.
Ko, Y. Urban Form and Residential Energy Use. J. Plan. Lit. 2013, 28, 327–351.
Chu, S.; Majumdar, A. Opportunities and challenges for a sustainable energy future. Nature 2012, 488, 294–303.
Yelisieieva, O.; Lyzhnyk, Y.; Stolietova, I.; Kutova, N. Study of Best Practices of Green Energy Development in the EU Countries Based on Correlation and Bagatofactor Autoregressive Forecasting. Econ. Innov. Econ. Res. J. 2023, 11.
Shambalid, A. Energy Efficiency in Residential Buildings. Delhi Technological University, 2023. Available online: http://dspace.dtu.ac.in:8080/jspui/handle/repository/20094 (accessed on 8 June 2023).
Kitsopoulou, A.; Zacharis, A.; Ziozas, N.; Bellos, E.; Iliadis, P.; Lampropoulos, I.; Chatzigeorgiou, E.; Angelakoglou, K.; Nikolopoulos, N. Dynamic Energy Analysis of Different Heat Pump Heating Systems Exploiting Renewable Energy Sources. Sustainability 2023, 15, 11054.
Sastry, L.; Karri, S.P.K. Smart Home Energy Management Using Non-intrusive Load Monitoring. In Sustainable Energy Solutions with Artificial Intelligence, Blockchain Technology, and Internet of Things; CRC Press: Boca Raton, FL, USA, 2023; p. 30. ISBN 9781003356639.
Mateen, A.; Wasim, M.; Ahad, A.; Ashfaq, T.; Iqbal, M.; Ali, A. Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm. Alex. Eng. J. 2023, 77, 593–611.
Elkadeem, M.R.; Abido, M.A. Optimal planning and operation of grid-connected PV/CHP/battery energy system considering demand response and electric vehicles for a multi-residential complex building. J. Energy Storage 2023, 72, 108198.
Porsani, G.B.; Casquero-Modrego, N.; Trueba, J.B.E.; Bandera, C.F. Empirical evaluation of EnergyPlus infiltration model for a case study in a high-rise residential building. Energy Build. 2023, 296, 113322.
Coakley, D.; Raftery, P.; Keane, M. A review of methods to match building energy simulation models to measured data. Renew. Sustain. Energy Rev. 2014, 37, 123–141.
Irfan, M.; Zhao, Z.-Y.; Li, H.; Rehman, A. The influence of consumers’ intention factors on willingness to pay for renewable energy: A structural equation modeling approach. Environ. Sci. Pollut. Res. 2020, 27, 21747–21761.
Dehghanpour, K.; Nehrir, M.H.; Sheppard, J.W.; Kelly, N.C. Agent-Based Modeling in Electrical Energy Markets Using Dynamic Bayesian Networks. IEEE Trans. Power Syst. 2016, 31, 4744–4754.
Ferkingstad, E.; Løland, A.; Wilhelmsen, M. Causal modeling and inference for electricity markets. Energy Econ. 2011, 33, 404–412.
Du, M.; Wu, F.; Ye, D.; Zhao, Y.; Liao, L. Exploring the effects of energy quota trading policy on carbon emission efficiency: Quasi-experimental evidence from China. Energy Econ. 2023, 124, 106791.
Frederiks, E.R.; Stenner, K.; Hobman, E.V.; Fischle, M. Evaluating energy behavior change programs using randomized controlled trials: Best practice guidelines for policymakers. Energy Res. Soc. Sci. 2016, 22, 147–164.
Altinay, G.; Karagol, E. Structural break, unit root, and the causality between energy consumption and GDP in Turkey. Energy Econ. 2004, 26, 985–994.
Zhong-gui, M.; Xiao-han, X.; Xue-er, L. Three analytical frameworks of causal inference and their applications. Chin. J. Eng. 2022, 44, 1231–1243.
Pearl, J. The seven tools of causal inference, with reflections on machine learning. Commun. ACM 2019, 62, 54–60.
Textor, J.; van der Zander, B.; Gilthorpe, M.S.; Liśkiewicz, M.; Ellison, G.T. Robust causal inference using directed acyclic graphs: The R package ‘dagitty’. Int. J. Epidemiol. 2016, 45, 1887–1894.
Dinmohammadi, F.; Han, Y.; Shafiee, M. Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms. Energies 2023, 16, 3748.
Bhushan, N.; Steg, L.; Albers, C. Studying the effects of intervention programmes on household energy saving behaviours using graphical causal models. Energy Res. Soc. Sci. 2018, 45, 75–80.
Soytas, U.; Sari, R. Energy consumption and GDP: Causality relationship in G-7 countries and emerging markets. Energy Econ. 2003, 25, 33–37.
Chen, X.; Abualdenien, J.; Singh, M.M.; Borrmann, A.; Geyer, P. Introducing causal inference in the energy-efficient building design process. Energy Build. 2022, 277, 112583.
Girish, R.; Yimin, Z.; Supratik, M. Application of Causal Inference to the Analysis of Occupant Thermal State and Energy Behavioral Intentions in Immersive Virtual Environments. Front. Sustain. Cities 2021, 3, 730474.
Pan, X.; Ai, B.; Li, C.; Pan, X.; Yan, Y. Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China. Technol. Forecast. Soc. Chang. 2019, 144, 428–435.
Pflugradt, N.D. Modellierung von Wasser und Energieverbräuchen in Haushalten. Ph.D. Dissertation, Technische Universität Chemnitz, Chemnitz, Germany, 2016.
U.S. Department of Energy. EnergyPlusTM Version 9.4.0 Documentation Guide for Interface Developers; U.S. Department of Energy: Washington, DC, USA, 2020.
Lund, H.; Zinck Thellusfen, J. EnergyPLAN—Advanced Energy Systems Analysis Computer Model. 2020. Available online: https://zenodo.org/record/4001541 (accessed on 10 June 2023).
Riederer, P. Ninth International IBPSA Conference Montréal, Canada 15–18 August 2005, Matlab/Simulink for Building and Hvac Simulation—State of the Art, Centre Scientifique et Technique du Bâtiment, 84, Avenue Jean Jaurès, 77421 Marne la Vallée Cedex 2, France. Available online: http://www.ibpsa.org/proceedings/BS2005/BS05_1019_1026.pdf (accessed on 11 June 2023).
Simscape™ Electrical™ User’s Guide (Specialized Power Systems)© COPYRIGHT 1998–2019 by Hydro-Québec and The MathWorks, Inc. The MathWorks, Inc.: Apple Hill DriveNatick, MA, USA. Available online: https://toaz.info/doc-view-2 (accessed on 3 September 2023).
Beckman, W.A.; Broman, L.; Fiksel, A.; Klein, S.A.; Lindberg, E.; Schuler, M.; Thornton, J. TRNSYS The most complete solar energy system modeling and simulation software. Renew. Energy 1994, 5, 486–488.
Jayathissa, P. Design and Assessment of Adaptive Photovoltaic Envelopes. Chapter 3.2.3 RC Model for Building Energy Demand. Ph.D. Thesis, 2017; pp. 33–35. Available online: https://www.research-collection.ethz.ch/handle/20.500.11850/212017 (accessed on 3 September 2023).
Beausoleil-Morrison, I.; Kummert, M.; Macdonald, F.; Jost, R.; McDowell, T.; Ferguson, A. Demonstration of the new ESP-r and TRNSYS co-simulator for modelling solar buildings. Energy Procedia 2012, 30, 505–514.
Bjrsell, N.; Bring, A.; Eriksson, L.; Grozman, P.; Lindgren, M.; Sahlin, P.; Shapovalov, E.; Ab, B. IDA Indoor Climate and Energy. In Proceedings of the Building Simulation; Volume 2, pp. 1035–1042. Available online: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1505526ddf0183237ed34e83ec1c7011efe0b5bf (accessed on 3 September 2023).
Wetter, M.; Zuo, W.; Nouidui, T.S.; Pang, X. Modelica Buildings library. J. Build. Perform. Simul. 2014, 7, 253–270.
Krajzewicz, D.; Erdmann, J.; Behrisch, M.; Bieker, L. Recent Development and Applications of SUMO-Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 2012, 5, 128–138.
Horni, A.; Nagel, K.; Axhausen, K.W. The Multi-Agent Transport Simulation MATSim; Ubiquity Press: London, UK, 2016.
Fellendorf, M. VISSIM: A Microscopic Simulation Tool to Evaluate Actuated Signal Control including Bus Priority. In Proceedings of the 64th ITE Annual Meeting, Dallas, TX, USA, 16–19 October 1994; pp. 1–9.
Siskos, P.; Capros, P. Primes-Tremove: A Transport Sector Model for Long-Term Energy-Economy-Environment Planning for EU. In Proceedings of the 20th Conference of the International Federation of Operational Research Societies, Barcelona, Spain, 13–18 July 2014.
Crawley, D.B.; Lawrie, L.K.; Winkelmann, F.C.; Buhl, W.; Huang, Y.; Pedersen, C.O.; Strand, R.K.; Liesen, R.J.; Fisher, D.E.; Witte, M.J.; et al. EnergyPlus: Creating a new-generation building energy simulation program. Energy Build. 2001, 33, 319–331.
Andarini, R. The Role of Building Thermal Simulation for Energy Efficient Building Design. Energy Procedia 2014, 47, 217–226.
Krstić, H.; Teni, M. Review of Methods for Buildings Energy Performance Modelling. IOP Conf. Ser. Mater. Sci. Eng. 2017, 245, 042049.
Gabrielli, L.; Ruggeri, A.G. Developing a model for energy retrofit in large building portfolios: Energy assessment, optimization and uncertainty. Energy Build. 2019, 202, 109356.
Kong, D.; Yang, Y.; Sa, X.; Wei, X.; Zheng, H.; Shi, J.; Wu, H.; Zhang, Z. Evaluation of the Impact of Input-Data Resolution on Building-Energy Simulation Accuracy and Computational Load—A Case Study of a Low-Rise Office Building. Buildings 2023, 13, 861.
Nejadshamsi, S.; Eicker, U.; Wang, C.; Bentahar, J. Data sources and approaches for building occupancy profiles at the urban scale—A review. Build. Environ. 2023, 238, 110375.
Gillich, A.; Saber, E.M.; Mohareb, E. Limits and uncertainty for energy efficiency in the UK housing stock. Energy Policy 2019, 133, 110889.
Iria, J.; Soares, F. An energy-as-a-service business model for aggregators of prosumers. Appl. Energy 2023, 347, 121487.
Bukar, A.L.; Hamza, M.F.; Ayub, S.; Abobaker, A.K.; Modu, B.; Mohseni, S.; Brent, A.C.; Ogbonnaya, C.; Mustapha, K.; Idakwo, H.O. Peer-to-peer electricity trading: A systematic review on current developments and perspectives. Renew. Energy Focus 2023, 44, 317–333.
Okur, Ö.; Heijnen, P.; Lukszo, Z. Aggregator’s business models in residential and service sectors: A review of operational and financial aspects. Renew. Sustain. Energy Rev. 2021, 139, 110702.
IRENA. Innovation Landscape Brief: Community-Ownership Models; International Renewable Energy Agency: Masdar City, Abu Dhabi, 2020.
IRENA. Innovation Landscape Brief: Pay-as-You-Go Models; International Renewable Energy Agency: Masdar City, Abu Dhabi, 2020.
Gitelman, L.; Kozhevnikov, M. New Business Models in the Energy Sector in the Context of Revolutionary Transformations. Sustainability 2023, 15, 3604.
Wang, C.; Ferrando, M.; Causone, F.; Jin, X.; Zhou, X.; Shi, X. Data acquisition for urban building energy modeling: A review. Build. Environ. 2022, 217, 109056.
Hong, T.; Chen, Y.; Luo, X.; Luo, N.; Lee, S.H. Ten questions on urban building energy modeling. Build. Environ. 2019, 168, 106508.
Du, S.; Zhang, X.; Jin, X.; Zhou, X.; Shi, X. A review of multi-scale modelling, assessment, and improvement methods of the urban thermal and wind environment. Build. Environ. 2022, 213, 108860.
Yang, S.; Wang, L.; Stathopoulos, T.; Marey, A.M. Urban microclimate and its impact on built environment—A review. Build. Environ. 2023, 238, 110334.

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