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HVAC System: Load Forecasting, System Modeling, Optimal Control and Flexible Interaction

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1605

Special Issue Editors

Prof. Dr. Xiaosong Zhang

E-Mail Website
Guest Editor
School of Energy and Environment, Southeast University, Nanjing, China
Interests: HVAC system; refrigeration heat pump; dehumidification; district energy system
Dr. Shifang Huang

E-Mail Website
Guest Editor
School of Energy and Environment, Southeast University, Nanjing 210096, China
Interests: HVAC optimization; heat pump; district energy systems; flexible interaction; pumped thermal electricity storage
Dr. Muxing Zhang

E-Mail Website
Guest Editor
School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: HVAC system; phase change materials; energy systems & sustainability; thermal management for electronics; computational micro-/nano-scale heat transfer

Special Issue Information

Dear Colleagues,

As a critical contributor to energy consumption and carbon emissions in buildings and industrial sectors, HVAC systems play a pivotal role in advancing sustainability through enhanced energy conservation and decarbonization capabilities. With continuous improvements in societal productivity and living standards, HVAC infrastructure across these domains is undergoing transformative shifts: the escalating installed capacity and system scale are driving substantial increases in aggregate energy demand, while the heightened electrification rates amplify stochastic load timing patterns, posing significant challenges to grid stability. Driven by advancements in load forecasting, system modeling, and optimal control technologies, HVAC systems are accelerating their transition toward digitized and intelligent operational paradigms. Notably, through deep operational integration with building structures, pipeline networks, and industrial processes, modern HVAC systems are transcending their conventional role as passive energy consumers. By leveraging thermal inertia for flexible grid interaction, these systems are evolving into energy hubs equipped with bidirectional regulation capabilities, marking a fundamental shift from energy-intensive operations to dynamic energy management architectures.

This Special Issue aims to present and disseminate the most recent advances related to the theory, design, modeling, application, control, and flexible interaction of all types of HVAC systems.

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

  • Load forecasting;
  • Equipment and system modeling;
  • Advanced HVAC system;
  • System design and planning;
  • Performance evaluation and analysis​;
  • Optimal control;
  • Flexible interaction with power grid.

Prof. Dr. Xiaosong Zhang
Dr. Shifang Huang
Dr. Muxing Zhang
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

  • HVAC system
  • load forecasting
  • system modeling
  • optimal control
  • flexible interaction

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Published Papers (2 papers)

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Research

22 pages, 4755 KB  
Article
Comparative Assessment of Supervised Machine Learning Models for Predicting Water Uptake in Sorption-Based Thermal Energy Storage
by Milad Tajik Jamalabad, Elham Abohamzeh, Daud Mustafa Minhas, Seongbhin Kim, Dohyun Kim, Aejung Yoon and Georg Frey
Energies 2026, 19(7), 1619; https://doi.org/10.3390/en19071619 - 25 Mar 2026
Viewed by 335
Abstract
In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. [...] Read more.
In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. Experimental studies involve physical testing and measurements but are often costly and time-consuming. Numerical simulations are more flexible and cost-effective, though they can require significant computational resources for large or complex systems. To address these challenges, researchers are increasingly employing various machine learning techniques, which offer strong potential for data analysis and predictive modeling. In this study, CFD-based sorption simulations are integrated with machine learning models to predict the spatiotemporal evolution of water uptake. Several ML techniques including support vector regression (SVR), Random Forest, XGBoost, CatBoost (gradient boosting decision trees), and multilayer perceptron neural networks (MLPs) are evaluated and compared. A fixed-bed reactor equipped with fins and tubes is considered within a closed adsorption thermal storage system. Numerical simulations are conducted for three different fin lengths (10 mm, 25 mm, and 35 mm) to generate a comprehensive dataset for training the ML models and capturing the complex temporal evolution of water uptake, thereby enabling predictions for unseen fin geometries. The results indicate that neural network-based models achieve superior predictive performance compared to the other methods. For water uptake training, the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination R2 are approximately 2.83, 4.37, and 0.91, respectively. The predicted water uptake shows close agreement with the numerical simulation results. For the prediction cases, the MAE, MSE, and R2 values are approximately 1.13, 1.2, and 0.8, respectively. Overall, the study demonstrates that machine learning models can accurately predict water uptake beyond the training dataset, indicating strong generalization capability and significant potential for improving thermal management system design. Additionally, the proposed approach reduces simulation time and computational cost while providing an efficient and reliable framework for modeling complex sorption processes in thermal energy storage systems. Full article
(This article belongs to the Special Issue HVAC System: Load Forecasting, System Modeling, Optimal Control and Flexible Interaction)
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22 pages, 6187 KB  
Article
Device Modeling Method for the Entire Process of Energy-Saving Retrofit of a Refrigeration Plant
by Xuanru Xu, Lun Zhang, Jun Chen, Qingbin Lin and Junjie Chen
Energies 2025, 18(15), 4147; https://doi.org/10.3390/en18154147 - 5 Aug 2025
Viewed by 776
Abstract
With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the [...] Read more.
With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article
(This article belongs to the Special Issue HVAC System: Load Forecasting, System Modeling, Optimal Control and Flexible Interaction)
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