Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features

Abstract

To fully exploit the rich characteristic variation laws of an integrated energy system (IES) and further improve the short-term load-forecasting accuracy, a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with multi-features. Feature expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity, far and near time periods. The Gaussian mixture model (GMM) was used to divide the scene of the comprehensive load day, and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be forecasted. Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a “dynamic similar day” by weighting. The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM. Comparing the static features as input and the selection method of similar days based on non-extended single features, the effectiveness of the proposed prediction method was verified.