Application of Machine Learning in Building Performance and Building Stock Research

Dear Colleagues,

Thanks to the rapid increase of data availability, as well as increasing computational capacities and simplified programing methods, machine learning tools are being progressively applied in the research fields of building performance and building stock research. This Special Issue will provide a space for this emerging research topic.

More specifically, the purpose of this Special Issue is to gather scientific ideas, research methods, and innovative applications related to ”Application of Machine Learning in Building Performance and Building Stock Research”.

Examples of relevant techniques are:

• Image recognition for detection of building features (satellite, drone or open data);
• Statistical prediction of unknown building features in otherwise comprehensive datasets;
• Pattern recognition in flowing data logs of measurements in buildings;
• Decision support tools for building owners and decision makers using machine learning.

References:

Bilal, Muhammad, Lukumon O Oyedele, Junaid Qadir, Kamran Munir, Saheed O Ajayi, Olugbenga O Akinade, Hakeem A Owolabi, Hafiz A Alaka, and Maruf Pasha. 2016. “Big Data in the Construction Industry: A Review of Present Status, Opportunities, and Future Trends.” Advanced Engineering Informatics 30 (3): 500–521.

Hong, Tianzhen, Zhe Wang, Xuan Luo, and Wanni Zhang. 2020. “State-of-the-Art on Research and Applications of Machine Learning in the Building Life Cycle.” Energy and Buildings, 109831 

Dr. Mikael Mangold
Guest Editor

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