Prediction and analysis of short-term load forecasting model based on similar day clustering and catboost

Abstract

Accurate load forecasting provides a reference of vital importance for power generation and power dispatching systems. A short-term load forecasting model combining similar day clustering and CatBoost method is proposed in this paper. Similar day clustering is a method for evaluating the similarity between the days to be forecast and historical days. Similar day clustering is based on K-means clustering which can classify samples with similar features into the same category. Then, CatBoost is used to build load forecasting models. CatBoost enables us to directly handle categorical features without pre-processing. This paper utilized the information collected by a substation in Hangzhou as the data set, and compare the proposed model with the existing forecasting model based on Autoregressive Moving Average, Gradient Boosting Decision Tree, CatBoost, Long Short- Term Memory. The experimental results show that the proposed model provides more accurate results compared with other models in terms of MAPE, RMSE.