Improved load forecasting method based on TCN-BiGRU and attention mechanism

Abstract

Abstract The accurate anticipation of electricity demand in the short term is crucial for ensuring the safe operation of the power grid and optimizing the power system. However, the existing prediction algorithms often suffer from limited accuracy. To address this issue, this study proposes a novel prediction model called TCN-BiGRU-Attention. This model utilizes TCN to extract features from the original load prediction data, processes the long-term correlations in time series data using GRU, and incorporates attention mechanisms to enhance the utilization of global correlation information. Compared to traditional prediction methods, this prediction model has better accuracy