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Urban Science
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Optimizing Urban Building Energy Analysis and Prediction: Evidence from AI,...
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Optimizing Urban Building Energy Analysis and Prediction: Evidence from AI, Big Data, and Machine Learning

A special issue of Urban Science (ISSN 2413-8851). This special issue belongs to the section "Intelligent Cities and Technology".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 8416

Special Issue Editors

Dr. Xuechen Gui

E-Mail Website
Guest Editor
School of Urban Design, Wuhan University, Wuhan 430072, China
Interests: green building; building energy conservation; big data analysis of building performance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Liu

E-Mail Website
Guest Editor
School of Urban Design, Wuhan University, Wuhan 430072, China
Interests: architectural design; urban design; urban heritage protection and urban renewal

Special Issue Information

Dear Colleagues,

As cities continue to expand and energy demands rise, efficient urban energy management has become a critical challenge for sustainable development. Traditional energy analysis methods often struggle to capture the complexity of urban environments, where diverse factors such as building typologies, climate variations, and human behavior influence energy consumption patterns. Recent advancements in artificial intelligence (AI), big data, and machine learning (ML) offer powerful tools to enhance our understanding of urban energy use, optimize energy efficiency, and support data-driven decision-making for smart cities. By leveraging these technologies, researchers and practitioners can develop predictive models, identify optimization strategies, and create intelligent energy management systems that contribute to carbon neutrality and resilient urban infrastructure.

This Special Issue aims to explore the integration of AI, big data, and machine learning into urban energy analysis and prediction. It aligns with the journal’s scope by addressing innovative computational approaches that improve energy efficiency, sustainability, and resilience in urban environments. The Special Issue will highlight cutting-edge methodologies and applications that enhance energy forecasting, real-time monitoring, and automated decision-making for urban energy systems.

We invite original research articles and review papers on topics including, but not limited to, the following:

  • AI-driven energy consumption forecasting in urban areas;
  • Big data analytics for urban energy management and decision-making;
  • Machine learning models for energy efficiency optimization in smart cities;
  • Predictive analytics for urban energy demand and supply balancing;
  • Digital twins and AI-enhanced simulations for urban energy performance;
  • Real-time energy monitoring and intelligent control systems;
  • The integration of AI and IoT into smart grids and energy networks;
  • Policy implications and urban planning strategies for AI-powered energy management.

We welcome to this Special Issue both theoretical and applied research that contributes to the advancement of AI, big data, and machine learning in urban energy analysis. Contributions that bridge the gap between research and real-world applications, as well as interdisciplinary studies, are particularly encouraged.

We look forward to your valuable contributions.

Dr. Xuechen Gui
Prof. Dr. Wei Liu
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. Urban Science is an international peer-reviewed open access monthly 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 1800 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

  • urban building energy performance
  • renewable energy
  • urban building energy efficiency
  • building energy prediction

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

Published Papers (3 papers)

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Research

30 pages, 8655 KB  
Article
GAN-MIGA-Driven Building Energy Prediction and Block Layout Optimization: A Case Study in Lanzhou, China
by Xinwei Guo, Shida Wang and Jingyi Li
Urban Sci. 2026, 10(2), 77; https://doi.org/10.3390/urbansci10020077 - 1 Feb 2026
Viewed by 805
Abstract
With the rapid urbanization in China, building energy consumption has become a critical challenge for sustainable urban development. Conventional simulation methods are computationally intensive and inefficient for large-scale urban layout optimization, highlighting the need for fast and reliable predictive approaches. Existing machine learning [...] Read more.
With the rapid urbanization in China, building energy consumption has become a critical challenge for sustainable urban development. Conventional simulation methods are computationally intensive and inefficient for large-scale urban layout optimization, highlighting the need for fast and reliable predictive approaches. Existing machine learning models often overlook spatial relationships among buildings and rely heavily on manual feature engineering, which limits their applicability at the urban block scale. To address these limitations, the study proposes a building energy consumption prediction model for urban blocks based on Generative Adversarial Networks (GANs), which preserves spatial information while significantly advancing computational speed. The optimal GAN model is further integrated with a Multi-Island Genetic Algorithm (MIGA) to form a GAN-MIGA optimization framework, which is applied to the layout optimization of a target urban block in Lanzhou. Key findings include: (1) the GAN model achieves an average prediction error of 6.8% compared with conventional energy simulations; (2) the GAN-MIGA framework reduces energy consumption by 48.78% relative to the worst-performing solution and by 22.53% compared with the original block layout; (3) the spatial distribution patterns of energy consumption predicted by the GAN are consistent with those obtained from traditional simulation methods; (4) the regression model derived from GAN-MIGA optimization results achieves an R2 value exceeding 0.84; and (5) building layout design strategies are formulated based on key morphological indicators in the regression model. Overall, this study demonstrates the effectiveness of the GAN-based method for urban scale building energy prediction and layout optimization. The proposed GAN-MIGA framework provides practical tools and theoretical support for energy-efficient design, policy formulation, and smart city development, contributing to more sustainable urban energy planning. Full article
(This article belongs to the Special Issue Optimizing Urban Building Energy Analysis and Prediction: Evidence from AI, Big Data, and Machine Learning)
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17 pages, 12279 KB  
Article
Spatiotemporal Assessment of Urban Heat Vulnerability and Linkage Between Pollution and Heat Islands: A Case Study of Toulouse, France
by Aiman Mazhar Qureshi, Khairi Sioud, Anass Zaaoumi, Olivier Debono, Harshit Bhatia and Mohamed Amine Ben Taher
Urban Sci. 2025, 9(12), 541; https://doi.org/10.3390/urbansci9120541 - 16 Dec 2025
Cited by 1 | Viewed by 806
Abstract
Urban heat vulnerability is an increasing public health concern, particularly in rapidly urbanizing regions of southern France. This study aims to quantify and map the Heat Vulnerability Index (HVI) for Toulouse and to analyze its temporal trends to identify high-risk zones and influencing [...] Read more.
Urban heat vulnerability is an increasing public health concern, particularly in rapidly urbanizing regions of southern France. This study aims to quantify and map the Heat Vulnerability Index (HVI) for Toulouse and to analyze its temporal trends to identify high-risk zones and influencing factors. The assessment integrates recent years’ remote sensing data of pollutant emissions, land use/land cover and land surface temperature, statistical data of climate-related mortalities, and socioeconomic and demographic factors. Following a detailed analysis of recent real-time air quality and weather data from multiple monitoring stations across the city of Toulouse, it was observed that Urban Pollution Island (UPI) and Urban Heat Island (UHI) are closely interlinked phenomena. Their combined effects can significantly elevate the annual mortality risk rate by an average of 2%, as calculated using AirQ+ particularly, in densely populated urban areas. Remote sensing data was processed using Google Earth Engine and all factors were grouped into three key categories: heat exposure, heat sensitivity, and adaptive capacity to derive HVI. Temporal HVI maps were generated and analyzed to identify recent trends, revealing a persistent increase in vulnerability across the city. Comparative results show that 2022 was the most critical summer period, especially evident in areas with limited vegetation and extensive use of heat-absorptive materials in buildings and pavements. The year 2024 indicates resiliency and adaptation although some areas remain highly vulnerable. These findings highlight the urgent need for targeted mitigation strategies to improve public health, enhance urban resilience, and promote overall human well-being. This research provides valuable insights for urban planners and municipal authorities in designing greener, more heat-resilient environments. Full article
(This article belongs to the Special Issue Optimizing Urban Building Energy Analysis and Prediction: Evidence from AI, Big Data, and Machine Learning)
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21 pages, 1723 KB  
Article
Transforming Chiller Plant Efficiency with SC+BAS: Case Study in a Hong Kong Shopping Mall
by Fong Ming-Lun Alan and Li Baonan Nelson
Urban Sci. 2025, 9(7), 253; https://doi.org/10.3390/urbansci9070253 - 2 Jul 2025
Cited by 1 | Viewed by 6115
Abstract
The imperative for building managers, in the face of high-density urban environments, is to drive existing chiller plants to greater operational efficiency through the application of advanced technological interventions. The case for applying Supervisory Control (SC) and a Building Automation System (SC+BAS) for [...] Read more.
The imperative for building managers, in the face of high-density urban environments, is to drive existing chiller plants to greater operational efficiency through the application of advanced technological interventions. The case for applying Supervisory Control (SC) and a Building Automation System (SC+BAS) for optimizing chiller plants is the subject of investigation here, through the lens of a typical commercial shopping mall in the high-density infrastructure of Hong Kong. The application of SC+BAS falls into the realm of advanced Trim/Respond algorithms coupled with sophisticated sequencing algorithms that allow for refined optimization of the chiller operations in response to the dynamic demands of urban infrastructure. The SC+BAS features an array of optimizations specifically for the chiller plant. Incentive parameters such as cooling capacity, energy usage, and Coefficient of Performance (COP) were thoroughly studied through 12 months’ worth of data, before and after the implementation of the SC+BAS. Empirical observations indicate a statistically significant 17.6% energy usage decrease, coupled with a 15.3% decrease in the related energy expenditure costs. Furthermore, the environmental impact is calculated, with an estimated 61.1 tons reduction in the amount of CO2 emissions, hence emphasizing the capacity for SC+BAS in offsetting the carbon footprint for commercial buildings. These data prove convincingly that the implementation of SC+BAS can increase the energy efficiency in chiller plants in commercial buildings, supporting the overall sustainability of the urban infrastructure. In turn, the authors suggest other areas for optimization through the advanced sequencing of chillers and demand-based cooling strategies. This highlights the ability of SC+BAS in creating more economical and green building operations regarding urban microclimates, occupant behavior patterns, and interactivity with the power grid, leading ultimately to the holistic optimization of chiller plant performance within the urban framework. Full article
(This article belongs to the Special Issue Optimizing Urban Building Energy Analysis and Prediction: Evidence from AI, Big Data, and Machine Learning)
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