Machine Learning for Building Performance: Modeling and Analysis for Building Assessment and Optimization
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: 15 January 2026 | Viewed by 64
Special Issue Editors
Interests: smart building; AI; building energy; grid-interactive building; commissioning
Special Issues, Collections and Topics in MDPI journals
Interests: AI; digital transformation; digital twin; autonomous system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) for building performance involves using advanced algorithms to analyze data and optimize energy efficiency, comfort, indoor environmental quality, and overall building operations. It enhances energy management through predictive maintenance, energy consumption forecasting, and anomaly detection, helping to identify inefficiencies and reduce energy costs. ML also ensures optimal indoor environmental quality by adjusting systems like HVAC and lighting based on occupancy, weather, and user preferences. In building automation, ML enables demand response management and integrates IoT devices to monitor and automate real-time adjustments for efficiency. It also aids in the design and retrofitting of buildings by simulating performance, recommending energy-saving upgrades, and optimizing renewable energy integration. These applications support the development of smarter, more adaptable buildings that balance energy use, comfort, indoor environmental quality, and building operations effectively. The benefits of implementing ML in building performance include significant cost savings, improved occupant comfort, and support for sustainability goals. By processing large datasets, ML provides insights to enhance decision-making and reduce energy consumption. This transition to data-driven, intelligent systems transforms buildings into proactive environments that adapt to changing conditions while meeting environmental and operational objectives.
This Special Issue aims to present recent advancements in building performance assessment and optimization through the application of machine learning modeling and analysis.
- Energy consumption forecasting;
- Anomaly detection;
- Data-driven modeling;
- Machine learning control;
- AI optimization;
- AI commissioning.
Dr. Sukjoon Oh
Dr. Seongjin Lee
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
- data-driven modeling
- machine learning control
- AI optimization
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