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Component-based machine learning for performance prediction in building design

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  • Geyer, Philipp
  • Singaravel, Sundaravelpandian

Abstract

Machine learning is increasingly being used to predict building performance. It replaces building performance simulation, and is used for data analytics. Major benefits include the simplification of prediction models and a dramatic reduction in computation times. However, the monolithic whole-building models suffer from a limited transfer of models and their data to other contexts. This imposes a vital limitation on the application of machine learning in building design. In this paper, we present a component-based approach that develops machine learning models not only for a parameterized whole building design, but for parameterized components of the design as well. Two decomposition levels, namely construction level components (wall, windows, floors, roof, etc.), and zone-level components, are examined. Results in test cases show that, depending on how far the cases deviate from the training case and its data, high prediction quality may be achieved with errors as low as 3.7% for cooling and 3.9% for heating.

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  • Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:1439-1453
    DOI: 10.1016/j.apenergy.2018.07.011
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    6. Ferrara, Maria & Della Santa, Francesco & Bilardo, Matteo & De Gregorio, Alessandro & Mastropietro, Antonio & Fugacci, Ulderico & Vaccarino, Francesco & Fabrizio, Enrico, 2021. "Design optimization of renewable energy systems for NZEBs based on deep residual learning," Renewable Energy, Elsevier, vol. 176(C), pages 590-605.
    7. Alsalemi, Abdullah & Ramadan, Mona & Bensaali, Faycal & Amira, Abbes & Sardianos, Christos & Varlamis, Iraklis & Dimitrakopoulos, George, 2019. "Endorsing domestic energy saving behavior using micro-moment classification," Applied Energy, Elsevier, vol. 250(C), pages 1302-1311.
    8. García Kerdan, Iván & Morillón Gálvez, David, 2020. "Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building," Applied Energy, Elsevier, vol. 280(C).
    9. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
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