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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Performance of Repeated Cross Validation for Machine Learning Models in Building Energy Analysis
verfasst von : Xiangfei Li, Baoquan Yin, Wei Tian, Yu Sun
Erschienen in: Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)
Verlag: Springer Singapore
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Abstract
Machine learning models have been widely used in building energy assessment to provide fast and reliable energy estimation. To validate these machine learning models, the cross-validation technique has been often used to estimate model accuracy. However, most cross-validation methods are used without repetition to have only one value that may have large variations due to different sampling seeds. Therefore, this paper applies repeated cross validation to provide reliable model accuracy with small variations. An office building with ten input variables is used as case study to demonstrate the performance of cross validation in building energy analysis. The results indicate that repeated cross validation can have stable results with sufficient sampling data available and medium fold number (ten in this case). At least 200 sampling number is required to obtain reliable model accuracy estimation. Ten times of cross validation is recommended to reduce the variations of model accuracy.