Short-term Load Prediction by CNN-LSTM Based on Attention Mechanism
Accurate power load forecasting is a key prerequisite for power system operation.In this paper,a new forecasting method is proposed to improve the accuracy of short-term power load forecasting.The correlation analysis between the influencing factors of four seasons and the load sequence is carried out by Spearman correlation coefficient.Combining the advantages that CNN is easy to handle high-dimensional data and can better tap into the implicit characteristics of the load sequence,and the attention mechanism can further assign weights to the input influencing factors,simulation experiments are carried out based on the main fac-tors of four seasons from the correlation analysis.The experimental results show that the combined CNN-LSTM-Attention network with feature-selective input has further improved the daily load prediction accuracy in different seasons compared with the CNN-LSTM-Attention network with full feature input and the CNN-LSTM network with full feature input.
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