Research on electricity consumption forecast based on mutual information and random forests algorithm

International Conference on Energy Engineering and Environmental Protection 2017

能源学;生态环境科学

Shi, Jing^1 ; Shi, Yunli^2 ; Tan, Jian^1 ; Zhu, Lei^1 ; Li, Hu^1

State Grid Jiangsu Electric Power Company Economic Research Institute, Jiangsu Nanjing

210008, China^1

Qingyuan Pumped Storage Power Generation Co., Ltd, Guangdong Qingyuan

511500, China^2

关键词: Average mutual information;    Correlation factors;    Electricity demands;    Electricity-consumption;    Forecasting models;    Mutual informations;    Power forecasting;    Prediction accuracy;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/121/5/052089/pdf
DOI  :  10.1088/1755-1315/121/5/052089

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Traditional power forecasting models cannot efficiently take various factors into account, neither to identify the relation factors. In this paper, the mutual information in information theory and the artificial intelligence random forests algorithm are introduced into the medium and long-term 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 was used for building 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, and the above methods are compared with the methods without regard to mutual information and the industries. The simulation results show that the above method is scientific, effective, and can provide higher prediction accuracy.