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Keywords

power system, medium and shortterm, random forests, mutual information

Abstract

Traditional power forecasting models can not efficiently take various factors into account, neither to identify the relation factors. In this paper, mutual information in information theory and the artificial intelligence random forests algorithm are introduced into the medium and shortterm electricity demand prediction. Mutual information can identify the high relation factors based on the value of average mutual information between a variety of variables and electricity demand. Different industries may be highly associated with different variables. The random forests algorithm is used to build the different industries forecasting models according to the different correlation factors. The data of electricity consumption in Jiangsu Province is taken as a practical example. In the example, the above methods are compared with the methods without mutual information and the industries. The simulation results show that the above method is scientific, effective, and can provide higher prediction accuracy.

DOI

10.19781/j.issn.16739140.2020.02.020

First Page

150

Last Page

156

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