Abstract
This paper uses a variety of machine learning methods to predict the hourly power consumption of a central air-conditioning system in a public building. It is found that the parameters of the central air-conditioning system are different at different times, so is the corresponding power consumption. The paper applies the time series to predict the power consumption on account of the time, which predicts the hourly power consumption based on historical time series data. Comparing the prediction accuracy of multiple machine learning methods, we find that the Gradient Boosting Regression Tree (GBRT), one of the ensemble learning methods, has the highest prediction accuracy.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (No. 61304199), Fujian Science and Technology Department (No. 2014H0008), Fujian Transportation Department (No. 2015Y0008), Fujian Education Department (No. JK2014033, JA14209, JA1532), and Fujian University of Technology (No. GYZ13125, 61304199, GY-Z160064). Many thanks to the anonymous reviewers, whose insightful comments made this a better paper.
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Gao, Sq., Zou, Fm., Jiang, Xh., Liao, L., Chen, Y. (2018). Prediction of Hourly Power Consumption for a Central Air-Conditioning System Based on Different Machine Learning Methods. In: Pan, JS., Wu, TY., Zhao, Y., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2017. Smart Innovation, Systems and Technologies, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-319-70730-3_25
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DOI: https://doi.org/10.1007/978-3-319-70730-3_25
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