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Buildings
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Building Energy Performance and Thermal Comfort: Synergies and Challenges
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Building Energy Performance and Thermal Comfort: Synergies and Challenges

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: 10 August 2025 | Viewed by 2962

Special Issue Editors

Dr. Ran Wang

E-Mail Website
Guest Editor
School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
Interests: building energy flexibility; building energy storage technology; building demand response design; green and low-carbon building technology; building fine design
Dr. Quanyi Lin

E-Mail
Guest Editor
School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
Interests: district heating and cooling system; integrated demand response; building thermal energy storage technology; energy system flexibility; energy efficient building

Special Issue Information

Dear Colleagues,

Rising global temperatures and extreme weather changes are driving greater demand for heating and cooling, which trigger increased electricity use and carbon emissions. Moreover, the surge in air conditioning load in extreme weather leads to a sharp rise in the peak load and peak-valley difference of the power grid, which brings great challenges to the balance of power supply and demand. Therefore, building energy now faces the dual challenge of providing a comfortable indoor environment while minimizing environmental impact. Striking a balance between comfort and energy saving has become an urgent task in the field of building energy.

The specific areas of focus within this Special Issue include the following:

  • Building tradeoff design;
  • Elasticity of energy demand;
  • Thermal comfort flexibility;
  • Building energy system control;
  • Building energy flexibility;
  • Energy demand response technology and policy.

Dr. Ran Wang
Dr. Quanyi Lin
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Buildings 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

  • thermal comfort flexibility
  • energy demand response
  • building energy flexibility
  • cooperative optimization
  • flexible control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

22 pages, 2304 KiB  
Article
Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest
by Hao Yang and Maoyu Ran
Buildings 2025, 15(14), 2539; https://doi.org/10.3390/buildings15142539 - 19 Jul 2025
Viewed by 278
Abstract
Establishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not [...] Read more.
Establishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not well suited for practical applications. In this study, real thermal sensation survey data were collected and used to first analyze the discrepancy between PMV model predictions and actual human thermal sensation. Subsequently, a simple thermal sensation prediction model was developed using multiple linear regression. More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. Finally, the best-performing model was further optimized using Bayesian methods to enhance hyperparameter tuning efficiency and improve the accuracy of personalized human thermal sensation prediction. Full article
(This article belongs to the Special Issue Building Energy Performance and Thermal Comfort: Synergies and Challenges)
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62 pages, 24318 KiB  
Article
Reconciling Urban Density with Daylight Equity in Sloped Cities: A Case for Adaptive Setbacks in Amman, Jordan
by Majd AlBaik, Rabab Muhsen and Wael W. Al-Azhari
Buildings 2025, 15(12), 2071; https://doi.org/10.3390/buildings15122071 - 16 Jun 2025
Viewed by 386
Abstract
Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow [...] Read more.
Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow patterns in Amman’s dense, mountainous urban fabric. Focusing on the Al Jubayhah district, a mixed-methods approach was used, combining field surveys, 3D modeling (Revit), and seasonal shadow simulations (March, September, December) to quantify daylight deprivation. The results reveal severe shading in winter (78.3% site coverage in December) and identify slope-dependent setbacks as a key determinant: for instance, a 15 m building on a 30% slope requires a 26.4 m rear setback to mitigate shadows, compared to 13.8 m on flat terrain. Over 39% of basements in the study area remain permanently shaded due to retaining walls, correlating with poor living conditions. The findings challenge Amman’s one-size-fits-all regulatory framework (Building Code No. 67, 1979), and we propose adaptive guidelines, including slope-adjusted setbacks, restricted basement usage, and optimized street orientation. This research underscores the urgency of context-sensitive urban policies in mountainous cities to balance developmental density with daylight equity, offering a replicable methodology for similar Mediterranean climates. Full article
(This article belongs to the Special Issue Building Energy Performance and Thermal Comfort: Synergies and Challenges)
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17 pages, 4856 KiB  
Article
Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control
by Dehan Liu, Jing Zhao, Yibing Wu and Zhe Tian
Buildings 2025, 15(10), 1654; https://doi.org/10.3390/buildings15101654 - 14 May 2025
Viewed by 519
Abstract
The optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) [...] Read more.
The optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) principle, implemented and validated on the integrated energy experimental platform. The experimental system simulates load generation and dissipation processes using a water tank, where hourly varying heating power output emulates the dynamic cooling loads of buildings. By regulating the chilled water system through different algorithms, the temperature tracking control performance and cooling supply regulation accuracy were rigorously validated. The control module was written in the Python 3.8 environment, and Niagara 4 software was used as an intermediate software to achieve data interaction and logical control with the laboratory system. The experimental results show that this algorithm can follow the hourly optimized parameters with a low overshoot in the short-term domain. Meanwhile, it can achieve the optimal control of cooling capacity and energy consumption in the long-term domain. Compared with the PID strategy, the temperature following control accuracy can be improved by 9.64%, and the cooling capacity can be saved by 6.24%. Compared with the day-ahead MPC algorithm, the temperature following control accuracy can be relatively improved by 16.52%, and the cooling capacity can be saved by 1.24%. Full article
(This article belongs to the Special Issue Building Energy Performance and Thermal Comfort: Synergies and Challenges)
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23 pages, 6030 KiB  
Article
Demand Response Strategy Optimization Method Based on Differentiated Comprehensive Benefit Model of Air-Conditioning Customers
by Boyang Li, Yuhan Wang, Houze Jiang, Ran Wang and Shilei Lu
Buildings 2025, 15(7), 1065; https://doi.org/10.3390/buildings15071065 - 26 Mar 2025
Viewed by 465
Abstract
Air-conditioning systems are critical demand response (DR) resources, yet conventional temperature adjustment strategies based on fixed setpoints often neglect users’ heterogeneous economic and comfort requirements. This paper proposes a DR strategy optimization method based on user-specific comprehensive benefit evaluation. Firstly, a quantitative model [...] Read more.
Air-conditioning systems are critical demand response (DR) resources, yet conventional temperature adjustment strategies based on fixed setpoints often neglect users’ heterogeneous economic and comfort requirements. This paper proposes a DR strategy optimization method based on user-specific comprehensive benefit evaluation. Firstly, a quantitative model integrating economic benefits and thermal comfort loss is established through the DR benefit mechanism. Subsequently, a DR strategy optimization model is established with indoor temperature setpoints as variables to maximize comprehensive benefits. Finally, comparative simulations involving 15 customers with varying benefit parameters (basic profitability and labor elasticity coefficients) demonstrate the proposed strategy’s superiority in load reduction and customers’ benefit over traditional fixed setpoint methods. The results indicate the following: (1) the optimized strategy achieves greater load reduction under most scenarios than traditional fixed-setpoint adjustment strategies; (2) all participants obtain enhanced comprehensive benefits compared with traditional strategies; and (3) customers with lower profitability and less dependency on labor show better responsiveness. This study improves DR participation incentives by balancing economic and comfort benefits, providing theoretical support for designing user-specific demand-side management policies in smart building applications. Full article
(This article belongs to the Special Issue Building Energy Performance and Thermal Comfort: Synergies and Challenges)
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23 pages, 8242 KiB  
Article
Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning
by Xin Chen, Huanchen Zhao, Beini Wang and Bo Xia
Buildings 2025, 15(6), 865; https://doi.org/10.3390/buildings15060865 - 10 Mar 2025
Cited by 1 | Viewed by 982
Abstract
As global climate change intensifies, the frequency and severity of extreme weather events continue to rise. However, research on semi-outdoor and transitional spaces remains limited, and transportation stations are typically not fully enclosed. Therefore, it is crucial to gain a deeper understanding of [...] Read more.
As global climate change intensifies, the frequency and severity of extreme weather events continue to rise. However, research on semi-outdoor and transitional spaces remains limited, and transportation stations are typically not fully enclosed. Therefore, it is crucial to gain a deeper understanding of the environmental needs of users in these spaces. This study employs machine learning (ML) algorithms and the SHAP (SHapley Additive exPlanations) methodology to identify and rank the critical factors influencing outdoor thermal comfort at tram stations. We collected microclimatic data from tram stations in Guangzhou, along with passenger comfort feedback, to construct a comprehensive dataset encompassing environmental parameters, individual perceptions, and design characteristics. A variety of ML models, including Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Random Forest (RF), and K-Nearest Neighbors (KNNs), were trained and validated, with SHAP analysis facilitating the ranking of significant factors. The results indicate that the LightGBM and CatBoost models performed exceptionally well, identifying key determinants such as relative humidity (RH), outdoor air temperature (Ta), mean radiant temperature (Tmrt), clothing insulation (Clo), gender, age, body mass index (BMI), and the location of the space occupied in the past 20 min prior to waiting (SOP20). Notably, the significance of physical parameters surpassed that of physiological and behavioral factors. This research provides clear strategic guidance for urban planners, public transport managers, and designers to enhance thermal comfort at tram stations while offering a data-driven approach to optimizing outdoor spaces and promoting sustainable urban development. Full article
(This article belongs to the Special Issue Building Energy Performance and Thermal Comfort: Synergies and Challenges)
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