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

A special issue of Urban Science (ISSN 2413-8851).

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1525

Special Issue Editors


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Guest Editor
School of Urban Design, Wuhan University, Wuhan 430072, China
Interests: green buildings; building energy efficiency; building performance; big data analysis; sustainable urban design; renewable energy

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

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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 1600 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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Published Papers (1 paper)

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Research

21 pages, 1723 KiB  
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
Viewed by 1341
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
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