Open AccessShort-term Load Prediction Based on the Combination of K-means and Random Forest
Author(s) -
Wei-Wei Chai,
Lei Qin,
Haiying Dong,
Chunshan Sun,
Wei Shen,
Shiyun Qiao
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2166/1/012027
Subject(s) - random forest , term (time) , cluster analysis , computer science , power (physics) , electricity , sample (material) , electric power system , engineering , artificial intelligence , electrical engineering , chemistry , physics , chromatography , quantum mechanics
Aiming at the problem that the power supply and distribution system runs at low load rate for a long time and wastes capacity due to the expansion of the power supply and distribution system, a short-term load forecasting method combining K-means and random forest is proposed. The proposed method divides power users into four categories based on electricity behavior, based on which the corresponding category load data is selected as the input sample of the random forest model to obtain short-term load prediction results. Example analysis shows that this method can ensure the rapid clustering accuracy, and effectively realize the short-term prediction of power load based on the random forest, to achieve the purpose of improving the load rate.