Short-term load forecasting based on temporal importance analysis and feature extraction

Abstract

Efficient and accurate short-term load forecasting plays a crucial role in ensuring the safe and stable operation of power systems and achieving economic management. This paper proposes an EIIR (Enhanced Importance Index Recognize) importance marking algorithm. This algorithm can extract the importance of each point in the load series, especially extreme points, so that machine learning models can focus on areas of high importance during training. This fills the research gap in the morphological characteristics of time series for peak and valley prediction. First, the K-Medoids algorithm is used to cluster the daily load curve, and then the EIIR algorithm is used to extract the numerical features of the extreme value points of various cluster centers. Then the importance features and historical data are reconstructed into a new feature set and input them into the CNN-LSTM hybrid neural network for prediction. Finally, the ISONE public power load data set is taken as an example for analysis and verification. In order to verify the reliability of the model prediction, the robustness of the model was analyzed and verified by adding input interference. The results show that this method can achieve more accurate short-term load prediction, and the model has good stability and robustness.