Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling

被引：102

作者：

Fan, Guo-Feng ^{[1
]}

Yu, Meng ^{[1
]}

Dong, Song-Qiao ^{[1
]}

Yeh, Yi-Hsuan ^{[2
]}

Hong, Wei-Chiang ^{[3
]}

机构：

[1] Ping Ding Shan Univ, Coll Math & Informat Sci, Ping Ding Shan 467000, Peoples R China

[2] Asia Eastern Univ Sci & Technol, Dept Informat Management, New Taipei 220303, Taiwan

[3] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China

来源：

UTILITIES POLICY | 2021年 / 73卷

关键词：

Support vector regression (SVR); Grey catastrophe (GC); Random forest (RF); Short term load forecasting; SVR MODEL; NEURAL-NETWORK; ALGORITHM; ENSEMBLE; DECOMPOSITION; MICROGRIDS;

D O I：

10.1016/j.jup.2021.101294

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper develops a novel short-term load forecasting model that hybridizes several machine learning methods, such as support vector regression (SVR), grey catastrophe (GC (1,1)), and random forest (RF) modeling. The modeling process is based on the minimization of both SVR and risk. GC is used to process and extract catastrophe points in the long term to reduce randomness. RF is used to optimize forecasting performance by exploiting its superior optimization capability. The proposed SVR-GC-RF model has higher forecasting accuracy (MAPE values are 6.35% and 6.21%, respectively) using electric loads from Australian-Energy-Market-Operator; it can provide analytical support to forecast electricity consumption accurately.

引用

页数：18

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