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|Title:||Fifty shades of black: Uncovering physical models from symbolic regressions for scalable building heat dynamics identification||Authors:||Leprince, J
|Issue Date:||17-Nov-2021||Publisher:||ACM||Citation:||Leprince, J, Miller, C, Frei, M, Madsen, H, Zeiler, W (2021-11-17). Fifty shades of black: Uncovering physical models from symbolic regressions for scalable building heat dynamics identification. BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation : 345-348. ScholarBank@NUS Repository. https://doi.org/10.1145/3486611.3491120||Abstract:||The rapid growth of machine learning (black-box) techniques and computing capacity has started to transform many research domains, including building performance analysis. However, physical interpretation of these models remains a challenge due to their opaque nature. This paper outlines an experiment to unveil analytical expressions from an open-source machine-learning-based algorithm, i.e., symbolic regression. From 241 residential buildings in the Netherlands, 50 unique analytical expressions were produced demonstrating overall better characterization accuracies than an XGBoost baseline, while providing a powerful mean of interpretability from model structures and coefficients. These insights present a starting point for further work towards highly scalable models yielding new characterizations of residential buildings.||Source Title:||BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation||URI:||https://scholarbank.nus.edu.sg/handle/10635/229340||ISBN:||9781450391146||DOI:||10.1145/3486611.3491120|
|Appears in Collections:||Staff Publications|
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