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Component-based machine learning for performance prediction in building design
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DOI：10.1016/j.apenergy.2018.07.011.png)
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摘要
En 中文
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.
Keyword:
Component-based machine learning
Systems engineering
Parametric systems modeling
Building performance prediction
Building simulation
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期刊
IF:
11
论文数:
2.6W
被引数:
17.8W
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