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
Interests: green buildings; building energy efficiency; building performance; big data analysis; sustainable urban design; renewable energy
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 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. 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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