Prediction of Hourly Power Consumption for a Central Air-Conditioning System Based on Different Machine Learning Methods
作者： Si-qi Gao , Fu-min Zou , Xin-hua Jiang , Lyuchao Liao , Yun Chen
DOI: 10.1007/978-3-319-70730-3_25
关键词:
摘要: This paper uses a variety of machine learning methods to predict the hourly power consumption central air-conditioning system in public building. It is found that parameters are different at times, so corresponding consumption. The applies time series on account time, which predicts based historical data. Comparing prediction accuracy multiple methods, we find Gradient Boosting Regression Tree (GBRT), one ensemble has highest accuracy.
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