Probability prediction of short-term user-level load based on random forest and kernel density estimation

Abstract

With the development of smart grids and the popularization of smart meters, grid companies have obtained a large amount of fine-grained user electricity consumption data, making it possible to forecast individual users’ electricity load. Traditional deterministic forecasting cannot measure the uncertainty of users’ future electricity consumption behavior. This paper aims to establish a short-term load probability forecasting model for individual users, which can output the probability prediction interval and the probability density curve. Firstly, historical data is applied to train a deterministic prediction model based on random forest. Then, when predicting the user load at a future time, the output of each tree in the forest is formed into a prediction set, and the kernel density estimation method is used to obtain the probability prediction result of the user load. The proposed RF-KDE method obtains promising results on a public dataset. It also shows the advantages of easy parameters adjustment and fast training speed. Besides, tests conducted on the public dataset confirm that the proposed method can be applied to users with different electricity consumption behaviors.

Keywords

• Short-term load forecasting
• Probabilistic forecasting
• Random forest
• Tree
• Kernel density estimation
• Probabilistic prediction interval