Sustainable Energy Transitions in Buildings and Cities: Insights from Data Analytics
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 October 2025 | Viewed by 32
Special Issue Editors
Interests: building information management (BIM); children's health and well-being and designing outdoor environments; ICT in built environment; building design and energy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The transition towards sustainable energy systems in buildings and cities is an urgent priority as we seek to address the challenges of climate change, urbanization, and energy consumption. This Special Issue invites original research that explores the role of data analytics in facilitating energy transitions in urban environments and buildings. We seek contributions that leverage innovative analytical tools and techniques—particularly focusing on big data, machine learning, and generative AI—to drive sustainable solutions in urban energy systems.
With the rapid growth of urban populations and the increasing demand for energy, the integration of smart technologies and sustainable practices has become crucial in ensuring that cities and buildings operate efficiently and responsibly. This Special Issue aims to uncover how data-driven approaches can optimize energy use, improve resource management, reduce carbon footprints, and enhance overall energy efficiency. Special attention will be given to the application of generative AI in energy system design, predictive analytics for building performance, and the real-time optimization of energy consumption in urban infrastructures.
We invite scholars, engineers, and practitioners to submit papers that address, but are not limited to, the following themes:
- Data analytics applications for energy-efficient building design and operation;
- The role of big data and machine learning in optimizing energy consumption in cities;
- Generative AI techniques for smart energy systems and predictive modeling in buildings;
- Sustainable urban planning through data-driven energy solutions;
- The integration of renewable energy technologies and smart grids in urban environments.
We welcome high-quality submissions that explore new methodologies, case studies, and theoretical advancements in these areas, with an emphasis on practical, actionable insights that can help cities and buildings achieve energy sustainability goals.
Prof. Dr. Lamine Mahdjoubi
Dr. Arslan Muhammad
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
- sustainable energy transitions
- data analytics
- generative AI
- urban energy systems
- smart cities
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