Short Term Electricity Price Prediction Based on Attention Mechanism for CNN-BIGRU

Accurate and timely short-term electricity price prediction can effectively guide the development and operation of the electricity market, promote the effective utilization and sustainable development of energy resources. This article proposes a CNN-BIGRU model based on attention mechanism for short-term electricity price prediction. Firstly, convolutional neural networks (CNN) are used to extract features from historical electricity price data, and then the extracted features are fed into a bidirectional gated recurrent unit (BIGRU) for sequence learning. In order to improve the model's attention to important time periods, an attention mechanism was introduced, enabling the model to automatically learn and focus on key time points. The experimental results on real electricity price datasets show that the proposed CNN-BIGRU model has good performance in short-term electricity price prediction tasks, and has higher accuracy and stability compared to traditional models.