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Energies
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Energy Modeling and Efficiency Optimization for Sustainable Building Systems
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Energy Modeling and Efficiency Optimization for Sustainable Building Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2515

Special Issue Editors

Dr. Liang Zhang

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85701, USA
Interests: urban system modeling; cyberinfrastructure of urban systems; AI in urban energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jianli Chen

E-Mail Website
Guest Editor
Department of Diasater Mitigation for Structures, College of Civil Engneering, Tongji University, Shanghai 200092, China
Interests: AI in building energy management; smart building; sustainable building; building and renewable energy integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing need to decarbonize the built environment and enhance energy efficiency in buildings has led to significant advancements in energy modeling, optimization techniques, and intelligent control strategies. As urbanization accelerates, sustainable building systems must integrate cutting-edge technologies to minimize energy consumption, reduce carbon emissions, and enhance occupant comfort. Recent developments in artificial intelligence (AI), physics-informed modeling, and digital twin technologies have opened new frontiers in optimizing energy use in buildings. These advances enable more accurate energy simulations, predictive control strategies, and seamless integration with renewable energy sources, thereby transforming how buildings interact with energy systems.

This Special Issue, "Energy Modeling and Efficiency Optimization for Sustainable Building Systems", aims to showcase state-of-the-art research in energy modeling, data-driven optimization, and intelligent building operations. This issue will focus on novel methodologies, case studies, and interdisciplinary perspectives that contribute to sustainable building design, predictive control, and resilience against climate change.

Topics of interest include, but are not limited to the following:

  • AI-driven energy modeling and optimization for sustainable building systems;
  • Smart building controls and model predictive control (MPC) for energy efficiency;
  • Integration of renewable energy sources with building systems;
  • Urban-scale energy modeling for smart cities and large-scale decarbonization;
  • Data-driven occupant behavior modeling and energy demand forecasting;
  • Cyberinfrastructure and digital twin technologies for real-time building operations;
  • HVAC system optimization and demand response strategies;
  • Impact of extreme weather and climate adaptation on building energy performance.

We invite researchers, engineers, and industry experts to submit original research articles, review papers, and case studies that highlight the latest advancements in energy modeling and efficiency optimization for sustainable building systems. 

Dr. Liang Zhang
Dr. Jianli Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable building systems
  • AI in energy modeling
  • smart building controls
  • renewable energy integration
  • urban-scale energy modeling
  • HVAC optimization
  • digital twins in buildings
  • climate adaptation in buildings

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Published Papers (2 papers)

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Research

29 pages, 7324 KB  
Article
A Hierarchical Control Framework for HVAC Systems: Day-Ahead Scheduling and Real-Time Model Predictive Control Co-Optimization
by Xiaoqian Wang, Shiyu Zhou, Yufei Gong, Yuting Liu and Jiying Liu
Energies 2025, 18(23), 6266; https://doi.org/10.3390/en18236266 - 28 Nov 2025
Viewed by 173
Abstract
Heating, ventilation, and air conditioning (HVAC) systems are the primary energy consumers in modern office buildings, with chillers consuming the most energy. As critical components of building air conditioning, the effective functioning of HVAC systems holds substantial importance for energy preservation and emission [...] Read more.
Heating, ventilation, and air conditioning (HVAC) systems are the primary energy consumers in modern office buildings, with chillers consuming the most energy. As critical components of building air conditioning, the effective functioning of HVAC systems holds substantial importance for energy preservation and emission mitigation. To enhance the operational performance of HVAC systems and accomplish energy conservation objectives, precise cooling load forecasting is essential. This research employs an office facility in Binzhou City, Shandong Province, as a case investigation and presents a day-ahead scheduling-based model predictive control (MPC) approach for HVAC systems, which targets minimizing the overall system power utilization. An attention mechanism-based long short-term memory (LSTM) neural network forecasting model is developed to predict the building’s cooling demand for the subsequent 24 h. Based on the forecasting outcomes, the MPC controller adopts the supply–demand equilibrium between cooling capacity and cooling demand as the central constraint and utilizes the particle swarm optimization (PSO) algorithm for rolling optimization to establish the optimal configuration approach for the chiller flow rate and temperature, thereby realizing the dynamic control of the HVAC system. To verify the efficacy of this approach, simulation analysis was performed using the TRNSYS simulation platform founded on the actual operational data and meteorological parameters of the building. The findings indicate that compared with the conventional proportional–integral–derivative (PID) control approach, the proposed day-ahead scheduling-based MPC strategy can attain an average energy conservation rate of 9.23% over a one-week operational period and achieve an energy-saving rate of 8.25% over a one-month period, demonstrating its notable advantages in diminishing building energy consumption. Full article
(This article belongs to the Special Issue Energy Modeling and Efficiency Optimization for Sustainable Building Systems)
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28 pages, 6260 KB  
Article
Development of Chiller Plant Models in OpenAI Gym Environment for Evaluating Reinforcement Learning Algorithms
by Xiangrui Wang, Qilin Zhang, Zhihua Chen, Jingjing Yang and Yixing Chen
Energies 2025, 18(9), 2225; https://doi.org/10.3390/en18092225 - 27 Apr 2025
Cited by 2 | Viewed by 2041
Abstract
To face the global energy crisis, the requirement of energy transition and sustainable development has emphasized the importance of controlling building energy management systems. Reinforcement learning (RL) has shown notable energy-saving potential in the optimal control of heating, ventilation, and air-conditioning (HVAC) systems. [...] Read more.
To face the global energy crisis, the requirement of energy transition and sustainable development has emphasized the importance of controlling building energy management systems. Reinforcement learning (RL) has shown notable energy-saving potential in the optimal control of heating, ventilation, and air-conditioning (HVAC) systems. However, the coupling of the algorithms and environments limits the cross-scenario application. This paper develops chiller plant models in OpenAI Gym environments to evaluate different RL algorithms for optimizing condenser water loop control. A shopping mall in Changsha, China, was selected as the case study building. First, an energy simulation model in EnergyPlus was generated using AutoBPS. Then, the OpenAI Gym chiller plant system model was developed and validated by comparing it with the EnergyPlus simulation results. Moreover, two RL algorithms, Deep-Q-Network (DQN) and Double Deep-Q-Network (DDQN), were deployed to control the condenser water flow rate and approach temperature of cooling towers in the RL environment. Finally, the optimization performance of DQN across three climate zones was evaluated using the AutoBPS-Gym toolkit. The findings indicated that during the cooling season in a shopping mall in Changsha, the DQN control method resulted in energy savings of 14.16% for the cooling water system, whereas the DDQN method achieved savings of 14.01%. Using the average control values from DQN, the EnergyPlus simulation recorded an energy-saving rate of 10.42% compared to the baseline. Furthermore, implementing the DQN algorithm across three different climatic zones led to an average energy savings of 4.0%, highlighting the toolkit’s ability to effectively utilize RL for optimal control in various environmental contexts. Full article
(This article belongs to the Special Issue Energy Modeling and Efficiency Optimization for Sustainable Building Systems)
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