Short-term Load Prediction Based on the Combination of K-means and Random Forest

Abstract

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.