Research on ultra-short-term load forecasting method based on PCA-MIC-LSTM

Abstract

This research presents a PCA-MIC-LSTM-based ultra-short-term electric load forecasting approach. This method uses Principal Component Analysis (PCA) technology to denoise power load data, uses the Maximal Information Coefficient (MIC) method to perform feature screening and correlation analysis on the processed data and finally selects some features with high correlation to input Long-Short-Term Memory Neural Network (LSTM) for training and modeling. Through actual case analysis, this method reduces the prediction error MAPE by 0.29% compared to the basic LSTM model. Compared with LightGBM, Xgboost, and SVR models, the prediction error MAPE is reduced by 0.16%, 0.11%, and 0.32%, respectively. It demonstrates the validity of the approach established in this investigation and provides technical support and a theoretical basis for scientific decision-making to optimize the precision of ultra-short-term electric load prediction in power systems.