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AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd...
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AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition

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: 31 July 2026 | Viewed by 50810

Special Issue Editors

Dr. Chaoqun Zhuang

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Guest Editor
Engineering Department, Cambridge University, Trumpington Street, Cambridge CB2 1PZ, UK
Interests: air conditioning systems; energy efficiency in buildings; AI and data analytics for the built environment
Special Issues, Collections and Topics in MDPI journals
Dr. Rui Guo

E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Engineering Sciences, KU Leuven, 3000 Leuven, Belgium
Interests: building/district energy use modeling; indoor environment; building performance improvement; advanced control of building energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Chong Zhang

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: built environment; building envelope; building ventilation; smart buildings
Special Issues, Collections and Topics in MDPI journals
Dr. Yunran Min

E-Mail Website
Guest Editor
School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361021, China
Interests: hybrid air conditioning systems; indirect evaporative cooling; enhanced heat and mass transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Building designs, operations, and commissioning are being revolutionized, with an increased emphasis on healthier, smarter, and more efficient environments. With the increasing penetration of smart sensors, the increasing electrification of buildings, and overwhelming amounts of data, artificial intelligence (AI) and big data analytics have shown extraordinary potential for improving building performance; however, the actual performance of emerging technologies has not been fully tested due to the complex, interdependent, and time-dependent stochastic nature of building systems spanning various types, functions, eras, and climates. 

In the context of this Special Issue, paper submissions related to the application of AI and data analytics to the built environment are welcome, especially in the domains of smart buildings, smart urban planning, and smart cities. Topics of interest include, but are not limited to, the following: smart digital technology for energy conservation and healthy buildings; transfer learning for modeling, diagnosis, and optimization in smart buildings; smart urban planning and city resilience; probabilistic modeling and risk-based decision support for building energy systems; data-driven ensemble AI models for energy and infection risk forecasting; and big data analytics for building and facility management, etc.

The purpose of this Special Issue is to develop AI-based guidelines and protocols for built environments, responding better to carbon neutrality and climate change.

Dr. Chaoqun Zhuang
Dr. Rui Guo
Dr. Chong Zhang
Dr. Yunran Min
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. 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

  • energy-efficient and healthy buildings
  • AI and data analytics
  • data-driven modelling
  • smart digital technology
  • smart buildings
  • smart urban planning
  • smart cities
  • intelligent architecture

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Related Special Issue

Published Papers (8 papers)

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Research

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19 pages, 5322 KB  
Article
Cooling-Fog Impacts on Microclimate and Thermal Comfort in Gwajeong Park, Busan
by Joowon Choi, Jaemoon Kim, Jaekyoung Kim, Taeyoon Kim and Soonchul Kwon
Buildings 2026, 16(3), 503; https://doi.org/10.3390/buildings16030503 - 26 Jan 2026
Viewed by 69
Abstract
Rapid urbanization and climate change have increased urban air temperatures and intensified the urban heat island effect through the expansion of impervious surfaces, loss of green areas, and high-density development. This study quantitatively evaluates the heat-mitigation performance and outdoor-thermal-comfort benefits of a high-pressure [...] Read more.
Rapid urbanization and climate change have increased urban air temperatures and intensified the urban heat island effect through the expansion of impervious surfaces, loss of green areas, and high-density development. This study quantitatively evaluates the heat-mitigation performance and outdoor-thermal-comfort benefits of a high-pressure micro-mist cooling-fog system installed in the Oncheoncheon area of Busan, South Korea. Five environmental sensors were deployed in Gwajeong Park to monitor the near-pedestrian air temperature and relative humidity, and thermal comfort was assessed using the Universal Thermal Climate Index and the Physiological Equivalent Temperature derived from meteorological variables. Both indices indicated improved thermal comfort during fog operation relative to the control condition. The relationship between air temperature and perceived thermal conditions was strong, while the mean radiant temperature exhibited substantial dispersion even under similar air temperatures. Higher global horizontal irradiance (GHI: incoming solar radiation on a horizontal surface) was associated with elevated mean radiant temperature, highlighting the importance of radiative load in pedestrian thermal stress. Overall, the findings provide field-based evidence that high-pressure micro-misting can improve outdoor thermal comfort and function as practical cooling infrastructure for heat-stress mitigation and urban climate resilience. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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15 pages, 3960 KB  
Article
Causal Discovery and Validation in Summer Weather Data with a Conceptual Extension to Cooling Energy Use
by Han-Gyeong Chu, Hye-Gi Kim and Deuk-Woo Kim
Buildings 2025, 15(23), 4248; https://doi.org/10.3390/buildings15234248 - 25 Nov 2025
Viewed by 385
Abstract
Traditional data-driven approaches emphasize input–output correlations and neglect dependencies among inputs, risking missed insights into key drivers of energy performance. Consequently, approaches that transcend correlation-centric analysis are warranted. Within this context, causal inference, which accounts for both statistical associations and temporal cause–effect relations, [...] Read more.
Traditional data-driven approaches emphasize input–output correlations and neglect dependencies among inputs, risking missed insights into key drivers of energy performance. Consequently, approaches that transcend correlation-centric analysis are warranted. Within this context, causal inference, which accounts for both statistical associations and temporal cause–effect relations, constitutes a promising direction. However, researchers cannot feasibly specify all causal relations relying solely on domain knowledge. Causal discovery is a data-driven methodology for analyzing causal relationships among variables, providing not only measures of association but also information on causal directionality. The authors employ two causal discovery algorithms—PC (Peter-Clark) and FCI (Fast Causal Inference)—on weather data. The discovered causal structures are compared, and two validation approaches are introduced to evaluate their statistical reliability; the authors also build on the identified causal structure to analyze the resulting causal pathways. The results show that both algorithms provide insights into causal relationships among variables, and the proposed validation approaches help establish the statistical reliability of the discovered structures. Moreover, the analysis of causal pathways indicates that causal effects can be identified and estimated with reliability. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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20 pages, 4919 KB  
Article
An ANN–CNN Hybrid Surrogate Model for Fast Prediction of 3D Temperature Fields in Large Datacenter Rooms
by Yuce Liu, Chaohui Zhou, Yue Hu, Wenkai Zhang, Wei He and Weiwei Guan
Buildings 2025, 15(22), 4042; https://doi.org/10.3390/buildings15224042 - 10 Nov 2025
Viewed by 994
Abstract
The increasing energy consumption of large datacenters, with cooling systems constituting a significant portion, calls for efficient thermal management strategies. Conventional computational fluid dynamics (CFD) methods, although accurate, are time-consuming for supporting real-time tasks in dynamic datacenter environments. Machine learning (ML)-based methods, particularly [...] Read more.
The increasing energy consumption of large datacenters, with cooling systems constituting a significant portion, calls for efficient thermal management strategies. Conventional computational fluid dynamics (CFD) methods, although accurate, are time-consuming for supporting real-time tasks in dynamic datacenter environments. Machine learning (ML)-based methods, particularly artificial neural network (ANN)-based surrogate models, have emerged as potential alternatives, but they struggle with generalization across diverse working conditions. Meanwhile, ML models’ performance in large datacenters still remains unclear. This research introduces a hybrid surrogate model combining ANNs and CNNs for the precise and rapid prediction of 3D temperature distributions in large datacenters. The proposed method incorporates an ANN for feature processing and a CNN for decoding spatial features, leveraging both to capture complex airflow patterns and temperature distributions under varying conditions. A dataset of 500 CFD-simulated temperature fields based on a real datacenter is established for model training and validation. The CFD method is evaluated by comparing the simulation results with experimental data. Results of the ML models’ performance indicate that the proposed hybrid surrogate model outperforms the conventional ANN model, reducing mean absolute error (MAE) by 87.44%. Additionally, the model is 300,000 times faster than CFD simulations, offering an efficient solution for further supporting real-time thermal management. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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31 pages, 2935 KB  
Article
A Novel Earth-to-Air Heat Exchanger-Assisted Ventilated Double-Skin Facade for Low-Grade Renewable Energy Utilization in Transparent Building Envelopes
by Zhanzhi Yu, Fei Liu, Wenke Sui, Rui Wang, Chong Zhang, Xiaoxiao Dong and Xinhua Xu
Buildings 2025, 15(20), 3655; https://doi.org/10.3390/buildings15203655 - 11 Oct 2025
Viewed by 829
Abstract
Transparent building envelopes significantly increase energy demands due to low thermal resistance and solar heat gain, while conventional double-skin facades may lead to overheating and high cooling loads in the summer. This study proposes a novel earth-to-air heat exchanger (EAHE)-assisted ventilated double-skin facade [...] Read more.
Transparent building envelopes significantly increase energy demands due to low thermal resistance and solar heat gain, while conventional double-skin facades may lead to overheating and high cooling loads in the summer. This study proposes a novel earth-to-air heat exchanger (EAHE)-assisted ventilated double-skin facade (VDSF) system utilizing low-grade shallow geothermal energy for year-round thermal regulation of transparent building envelopes. A numerical model of this coupled system was developed and validated to estimate the thermal performance of the EAHE-assisted VDSF system in a hot-summer-and-cold-winter climate. Parametric study was conducted to investigate the impact of some key design parameters on thermal performance of the EAHE-assisted VDSF system and further reveal recommended design parameters of this coupled system. The results indicate that the EAHE-VDSF system reduces annual accumulated cooling loads by 20.3% to 76.5% and heating loads by 19.6% to 47.1% in comparison to a conventional triple-glazed, non-ventilated facade. The cavity temperature of the VDSF decreases by 15 °C on average in the summer, effectively addressing the overheating issue in DSFs. The proposed coupled EAHE-VDSF system shows promising energy-saving potential and ensures stability and consistency in the thermal regulation of transparent building envelopes. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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28 pages, 4496 KB  
Article
Revealing the Driving Factors of Household Energy Consumption in High-Density Residential Areas of Beijing Based on Explainable Machine Learning
by Zizhuo Qi, Lu Zhang, Xin Yang and Yanxia Zhao
Buildings 2025, 15(7), 1205; https://doi.org/10.3390/buildings15071205 - 7 Apr 2025
Cited by 2 | Viewed by 1522
Abstract
This study explores the driving factors of household energy consumption in high-density residential areas of Beijing and proposes targeted energy-saving strategies. Data were collected through field surveys, questionnaires, and interviews, covering 16 influencing factors across household, building, environment, and transportation categories. A hyperparameter-optimized [...] Read more.
This study explores the driving factors of household energy consumption in high-density residential areas of Beijing and proposes targeted energy-saving strategies. Data were collected through field surveys, questionnaires, and interviews, covering 16 influencing factors across household, building, environment, and transportation categories. A hyperparameter-optimized ensemble model (XGBoost, RF, GBDT) was employed, with XGBoost combined with genetic algorithm tuning performing best. SHAP analysis revealed that key factors varied by season but included floor level, daily travel distance, building age, greening rate, water bodies, and household age. The findings inform strategies such as optimizing workplace–residence layout, improving building insulation, increasing green spaces, and promoting community energy-saving programs. This study provides refined data support for energy management in high-density residential areas, enhances the application of energy-saving technologies, and encourages low-carbon lifestyles. By effectively reducing energy consumption and carbon emissions during the operational phase of residential areas, it contributes to urban green development and China’s “dual carbon” goals. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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20 pages, 5706 KB  
Article
Numerical Study on the Risk of Infection in Adjacent Residential Spaces: Door Operation and the Impact of Outdoor Wind Speeds
by Xunmei Wu, Mengtao Han and Hong Chen
Buildings 2025, 15(1), 116; https://doi.org/10.3390/buildings15010116 - 31 Dec 2024
Cited by 1 | Viewed by 1817
Abstract
Infectious diseases have profoundly impacted global health and daily life. To control virus transmission, countries worldwide have implemented various preventive measures. A critical pathway for infection spread is cross-infection within households, especially among family members in the same or adjacent rooms. This study [...] Read more.
Infectious diseases have profoundly impacted global health and daily life. To control virus transmission, countries worldwide have implemented various preventive measures. A critical pathway for infection spread is cross-infection within households, especially among family members in the same or adjacent rooms. This study uses numerical simulations to examine aerosol transmission characteristics in adjacent spaces in home settings and assess associated infection risks. The study evaluated the effects of factors such as outdoor wind speed, door gap leakage, and door opening actions on aerosol concentration and infection risk across various areas. Key conclusions include the following: Under prolonged lack of ventilation, aerosol leakage through the door gap is minimal, with the average aerosol concentration outside the bedroom remaining low (<0.04). In the absence of ventilation, aerosol accumulation primarily occurs within the bedroom. Under ventilated conditions, door gap leakage may increase infection risk in adjacent areas, suggesting a stay duration of no more than 75 min to keep infection risk below 30%. The findings provide practical recommendations for airtight design and activity area selection within residential spaces, offering valuable guidance for effective infection control measures. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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29 pages, 15252 KB  
Article
Multi-Domain Environmental Quality of Indoor Mixed-Use Open Spaces and Insights into Healthy Living—A Quarantine Hotel Case Study
by Han Wang and Wenjian Pan
Buildings 2024, 14(11), 3443; https://doi.org/10.3390/buildings14113443 - 29 Oct 2024
Cited by 1 | Viewed by 2832
Abstract
In the post-pandemic context, data-driven design interventions that can endow architectural spaces with mixed-use and open characteristics that are adaptable and environmentally resilient are increasingly important. Ubiquitous semi-public architecture, such as hotel buildings, plays a crucial role in public health emergencies. Many hotels [...] Read more.
In the post-pandemic context, data-driven design interventions that can endow architectural spaces with mixed-use and open characteristics that are adaptable and environmentally resilient are increasingly important. Ubiquitous semi-public architecture, such as hotel buildings, plays a crucial role in public health emergencies. Many hotels adopt mixed-use and open room spatial layouts, integrating diverse daily functions into a single tiny space, fostering flexible utilization and micro-scale space sharing; however, these also introduce potential health risks. This study offers a comprehensive evaluation of the indoor environmental quality (IEQ) of a hotel room space and discusses feasible intervention strategies for healthier renovation and rehabilitation. Taking a hotel in Shenzhen as a case, a multi-domain environmental assessment was conducted during the COVID-19 quarantine period in the summer of 2022. The study examines the health risks inherent in the hotel’s guest room and the varying patterns of IEQ factors across the hotel’s domains, including volatile organic compound concentrations, physical environmental parameters, and heat stress indices. The results illustrate diverse change trends in the chemical, physical, and heat stress factors present in the tested quarantined hotel room space throughout a typical summer day. Although most of the examined environmental factors meet local and global standards, some problems draw attention. In particular, the PM2.5 concentration was generally observed to be above the World Health Organization (WHO) air quality guideline (AQG) standards, and the interior lighting did not meet required standards most of the time. Moreover, correlation and multiple regression analyses uncover significant influence by physical environmental conditions on the concentrations of chemical pollutants in the hotel room. The study preliminarily identifies that higher relative humidity could lead to a lower concentration of CO2 while a higher PM2.5 concentration. Wet bulb globe temperature (WBGT) was observed to positively affect CO2 concentration. Further, the results suggest that even with relatively rigorous initial adjustment and re-renovation, multi-domain environmental quality in air-conditioned quarantine hotel rooms should be monitored and ameliorated from time to time. Overall, this study offers a scientific foundation for healthier upgrades of existing hotel buildings as well as provides insights into achieving environmental resilience in newly constructed hotel buildings for the post-pandemic era. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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Review

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31 pages, 3777 KB  
Review
IoT—A Promising Solution to Energy Management in Smart Buildings: A Systematic Review, Applications, Barriers, and Future Scope
by Mukilan Poyyamozhi, Balasubramanian Murugesan, Narayanamoorthi Rajamanickam, Mohammad Shorfuzzaman and Yasser Aboelmagd
Buildings 2024, 14(11), 3446; https://doi.org/10.3390/buildings14113446 - 29 Oct 2024
Cited by 84 | Viewed by 40896
Abstract
The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and [...] Read more.
The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings: 2nd Edition)
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