Short-term load prediction with LSTM and FCNN models Based on attention mechanisms

Abstract

To address the significant load prediction discrepancies in microgrid economic dispatch, which result from substantial load fluctuations, a method for near-term load forecasting leveraging the attention mechanism is proposed, utilizing both LSTM and FCNN models. In this method, the attention mechanism is introduced to enhance the LSTM model’s ability to recognize and utilize key information, and the LSTM is used to process historical load data with time continuity selected by a sliding window, whereas the FCNN is used to process daily static features, such as the day’s maximum temperature, minimum temperature, and weather conditions. The outputs of the two networks are subsequently fused into a fully connected layer to generate the final load forecast. Training leverages six months of actual power load values and regional meteorological factor data. The forecast outcomes are then evaluated against both traditional and machine learning algorithms. The experimental findings highlight the superior prediction accuracy of the proposed model.