Abstract: Accurate and reliable short-term power load forecasting can optimize power dispatching, improve the utilization of power resources, and provide valuable references for the actual production of the power sector. With the diversification of power-using terminals and the influence of short-term factors such as weather and date, the load sequence shows obvious uncertainty and randomness. To address this, a novel two-stage short-term power load forecasting method based on improved kernel fuzzy C-mean clustering and bidirectional long- and short-term memory attention is proposed. In the first stage, the KFCM clustering method based on the joint improvement of principal component analysis and particle swarm optimization is used to group the load data points with similar electricity consumption characteristics into one class, which makes the model training more targeted. In the second stage, mete- orological and temporal features with high correlation are selected as inputs through the Pearson correlation coefficient. Meanwhile, to improve the prediction performance of the model, a temporal attention mechanism and a multi-head self-attention mechanism are introduced into the BiLSTM model. Finally, the proposed method is applied to the real power load dataset provided by Chongqing Electric Power Company in China. The experimental results show that the prediction accuracy is significantly improved compared with many different prediction methods.

Key words: load forecasting, fuzzy clustering, attention mechanism, neural network, feature screening