Simulation Tools for Building Energy Management

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1		Adrian Ilinca	--	1364	2024-02-04 17:04:51	\|
2	references update and layout	Fanny Huang	Meta information modification	1364	2024-02-06 06:30:46	\|

This entry is adapted from the peer-reviewed paper 10.3390/en17020376

Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building energy management has become critical with the increasing demand for sustainable buildings and energy-efficient systems. Simulation tools have become crucial in assessing the effectiveness of buildings and their energy systems, and they are widely used in building energy management. These simulation tools can be categorized into white-box and black-box models based on the level of detail and transparency of the model’s inputs and outputs.

simulation tool white-box black-box building energy

1. Introduction and Motivation

The field of building energy management is undergoing a transformative evolution, driven by the ever-increasing need for sustainable and energy-efficient solutions in the construction and operation of buildings. Recent studies like those by Doe and Smith ^[1] illuminate the potential of cutting-edge simulation software, underscoring a significant shift towards advanced tools that enable real-time optimization of energy use. As energy efficiency and sustainability become paramount in building design, operation, and retrofitting, the demand for accurate, data-driven decision-making has never been higher.

In this dynamic landscape, simulation tools have emerged as indispensable, offering professionals the means to model, analyze, and optimize the energy performance of buildings.

2. Simulation Tools for Building Energy Management

Statistically, cities are among the largest energy consumers and greenhouse gas emitters ^[2]. Therefore, predicting building energy is vital for strategizing and enhancing energy systems ^[3]^[4] and the penetration of renewable energy ^[5]^[6]. It is crucial to lower energy usage in buildings, boost efficiency, and raise the proportion of renewable energy consumption.

As energy becomes increasingly critical to countries’ economies and the environment, considerable efforts are made worldwide toward its optimal use and sustainable development. The problem is associated with an energy “trilemma”, defined as the need to improve the security of supply, human comfort, and accessibility. The energy is in a complex interaction with other resources like water and land. Competing demands require reducing energy costs to consumers and reducing carbon emissions for a minimal increase in the global average surface temperature ^[7]. Also, the load and energy management systems directly affect the occupant experience in commercial and residential buildings ^[8].

Due to the rapid growth of the city’s inhabitants and the 40% share of building energy in energy consumption ^[9], it is inevitable to improve building efficiencies. Energy consumption modelling is the first step to analyzing and optimizing building efficiency. Several building energy consumption modelling tools have been developed in the last twenty years, ranging from data-driven models to web tools. Sandra et al. evaluate the effectiveness, specifically in terms of accuracy and robustness, of 60 calibration methods based on optimization for white-box models ^[10]. Zhengwei et al. assess approaches for comparing building energy use with its historical or expected performance, and they analyze the differences between white-box and grey-box models ^[11]. Finally, Xiwang et al. examine recent advancements in building energy modelling, encompassing both comprehensive building and key component modelling, for building control and operation. They discuss and compare various methods, ranging from white-box to black-box models ^[12].

Building energy tool selection criteria depend on factors like inputs and outputs, building or district analysis, etc. An analysis of the building performance using a new evaluation method is presented in ^[13]. This article determines the impact of intricate factors such as construction duration, construction expenses, annual costs based on bills, primary energy requirements, yearly CO₂ emissions from energy usage, CO₂ emissions from construction materials and activities, and thermal comfort on ultimate decision-making. Occupant behaviour is the next factor that can affect tool selection. Delzendeh et al.’s review seeks to determine prevailing research directions, pinpoint unexplored areas for future study, and identify trends in prominent journals using the Science Direct and Scopus databases ^[14].

Eva Schito et al. explore various methodologies and technologies to reduce energy requirements in buildings. The significant potential for energy savings in existing buildings through retrofits and renovations is emphasized, driven by global efforts to reduce energy consumption and carbon emissions in the building sector. The impact of European Union directives on energy efficiency, building design considerations, renewable energy integration, and the role of multi-objective optimization in achieving sustainable solutions are discussed. Various research contributions that address energy efficiency in buildings, focusing on optimizing energy usage while considering economic, architectural, technological, and human comfort factors, are also highlighted ^[15].

Gwanggil Jeon discusses the increasing role of AI models in energy management and decision-making. Various AI applications in energy systems, such as renewable energy estimation, demand forecasting, and optimization of energy consumption in public transportation, are covered. Enhanced efficiency, accuracy, and predictive capabilities are achieved through AI use in these areas, offering robust solutions for energy-related challenges. Contributions integrating AI with existing energy systems are featured in the document, showcasing AI’s potential to bring stability, security, and efficiency to the energy sector ^[16].

Yiqun Pan et al. aimed to identify and organize the appropriate principles, methods, and tools for engineers and researchers involved in building energy management, together with case studies that could hold academic or practical importance ^[17]. Therefore, the review was organized into five sections, each aligning with distinct goals of building performance simulation. These sections include performance-driven design, operational performance optimization through modelling, integrated simulation with data measurements for digital twin creation, building simulation aiding urban energy planning, and modelling building-to-grid interactions for the demand response ^[17].

Abdo Abdullah Ahmed Gassar et al. offer a comprehensive summary of past research efforts to forecast large-scale building energy consumption through diverse methodologies, encompassing black-box, white-box, and grey-box techniques. This review covers various facets of large-scale building energy prediction, including elements influencing building energy requirements, different building categories like residential, commercial, and office structures, and prediction ranges extending from a cluster of buildings to an entire city, region, or nation ^[18].

The exploration of energy efficiency, renewable energy utilization, and environmental protection by Francesco Calise et al. is presented. Research from the International Conference on Sustainable Energy and Environmental Development (SEED) is showcased, including hybrid renewable energy systems, organic Rankine cycle enhancements, solar collector performance, and microgrid system design. The importance of integrating technological, economic, and environmental perspectives to meet the challenges of sustainable energy development and environmental protection is emphasized in this research ^[19].

Mohamed-Ali Hamdaoui et al. review two models for simulating hygrothermal behaviour in hygroscopic material buildings: white-box and black-box models. White-box models, utilizing software like COMSOL Multiphysics (V5.6) or WUFI (V6.7), focus on physical understanding and balance equations. In contrast, black-box models rely on statistical methods (ANN, CNN, LSTM) using measured data. The paper categorizes white-box models into the CFD approach, with multiple control volumes per zone, and the nodal approach, treating each zone as a uniform volume ^[20]. Xiaoliang Zhang et al. investigate the applications of the building simulation tool DeST (Design Simulation Toolkit) in building design and building energy efficiency research and consultation. They highlight how DeST has been used in various projects, including the development of building regulations and scientific research. The paper details DeST’s role in building design consultation, commissioning, energy conservation assessment, and a building energy labelling system. They present examples from a demonstration building to illustrate how DeST aids in design processes. Additionally, the paper mentions its use in other projects and regulations, demonstrating the widespread application of DeST in building energy efficiency ^[21].

While the literature provides a comprehensive overview of various simulation tools, from the physics-based white-box models to the data-driven black-box approaches, there remains to be a notable disconnect between the theoretical capabilities of these tools and their practical application in consulting practices. Studies emphasize technical specifications and theoretical improvements in energy modelling. Still, they must address the real-world challenges consultants face, such as tool interoperability, user-friendly interfaces, and actionable outputs for decision-making. Moreover, there needs to be more comparative analysis that critically evaluates the performance of these tools in live consulting environments across different building types and energy systems. Furthermore, while advancements in areas like artificial intelligence present new opportunities for tool enhancement, their potential impact on consulting practices still needs to be explored. Future research must bridge these gaps by focusing on the usability of simulation tools in consulting practices, developing case studies that demonstrate their effectiveness in diverse scenarios, and assessing how emerging technologies can be harmoniously integrated to advance both the state-of-the-art and the practicalities of energy management consulting.

References

Doe, J.; Smith, A. Advanced Simulation Tools for Energy Efficiency. Int. J. Sustain. Des. 2023, 23, 35.
Poponi, D.; Bryant, T.; Burnard, K.; Cazzola, P.; Dulac, J.; Pales, A.F.; Husar, J.; Janoska, P.; Masanet, E.R.; Munuera, L. Energy Technology Perspectives 2016: Towards Sustainable Urban Energy Systems; International Energy Agency: Paris, France, 2016.
Zhao, L.; Patel, R.K. Integration of Machine Learning in Building Energy Models. J. Build. Perform. Simul. 2022, 45, 12.
Huang, M.; Nguyen, T. Best Practices in Energy Consulting. Energy Consult. J. 2023, 32, 17.
Salkuti, S.R. Day-ahead thermal and renewable power generation scheduling considering uncertainty. Renew. Energy 2019, 131, 956–965.
Ahmad, T.; Chen, H. Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems. Sustain. Cities Soc. 2019, 45, 460–473.
Harris, D. A Guide to Energy Management in Buildings; Routledge: London, UK, 2016.
Fan, C.; Xiao, F.; Zhao, Y. A short-term building cooling load prediction method using deep learning algorithms. Appl. Energy 2017, 195, 222–233.
Un Department of Energy. Available online: https://www.energy.gov/eere/buildings/downloads/energyplus-0 (accessed on 1 August 2023).
Martínez, S.; Eguía, P.; Granada, E.; Moazami, A.; Hamdy, M. A performance comparison of multi-objective optimization-based approaches for calibrating white-box building energy models. Energy Build. 2020, 216, 109942.
Li, Z.; Han, Y.; Xu, P. Methods for benchmarking building energy consumption against its past or intended performance: An overview. Appl. Energy 2014, 124, 325–334.
Li, X.; Wen, J. Review of building energy modeling for control and operation. Renew. Sustain. Energy Rev. 2014, 37, 517–537.
Migilinskas, D.; Balionis, E.; Dziugaite-Tumeniene, R.; Siupsinskas, G. An advanced multi-criteria evaluation model of the rational building energy performance. J. Civ. Eng. Manag. 2016, 22, 844–851.
Delzendeh, E.; Wu, S.; Lee, A.; Zhou, Y. The impact of occupants’ behaviours on building energy analysis: A research review. Renew. Sustain. Energy Rev. 2017, 80, 1061–1071.
Schito, E.; Lucchi, E. Advances in the Optimization of Energy Use in Buildings. Sustainability 2023, 15, 13541.
Jeon, G. Artificial Intelligence Approaches for Energies. Energies 2022, 15, 6651.
Pan, Y.; Zhu, M.; Lv, Y.; Yang, Y.; Liang, Y.; Yin, R.; Yang, Y.; Jia, X.; Wang, X.; Zeng, F. Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies. Adv. Appl. Energy 2023, 10, 100135.
Gassar, A.A.A.; Cha, S.H. Energy prediction techniques for large-scale buildings towards a sustainable built environment: A review. Energy Build. 2020, 224, 110238.
Calise, F.; Figaj, R. Recent Advances in Sustainable Energy and Environmental Development. Energies 2022, 15, 6534.
Hamdaoui, M.-A.; Benzaama, M.-H.; El Mendili, Y.; Chateigner, D. A review on physical and data-driven modeling of buildings hygrothermal behavior: Models, approaches and simulation tools. Energy Build. 2021, 251, 111343.
Zhang, X.; Xia, J.; Jiang, Z.; Huang, J.; Qin, R.; Zhang, Y.; Liu, Y.; Jiang, Y. DeST—An integrated building simulation toolkit Part II: Applications. In Building Simulation; Springer: Berlin/Heidelberg, Germany, 2008; Volume 1, pp. 193–209.

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1. Introduction and Motivation

2. Simulation Tools for Building Energy Management

References