Short-term power load forecasting using SSA-CNN-LSTM method

Abstract

The short-term power load forecasting provides an essential foundation for the dispatching management of the power system, which is crucial for enhancing economy and ensuring operational stability. To enhance the precision of the short-term power load forecasting, this paper proposes a hybrid prediction algorithm based on sparrow search algorithm (SSA), convolutional neural network (CNN) and long short-term memory (LSTM). First, feature datasets are constructed based on date information, meteorological data, similar days. The CNN performs effective feature extraction on the data and feeds the results into the LSTM for time series data analysis. Second, eight key parameters are optimized by SSA for improving the prediction precision of the CNN-LSTM prediction model. Simulation results show that the R2 of the proposed model exhibits a substantial enhancement in comparison to other models, reaching 0.9919 and presents a remarkable decrease in MAPE resulting in a value of 1.2%. Furthermore, RMSE and MAE have decreased to 1.17MW and 0.97MW respectively. Therefore, the proposed method has the ability to improve the prediction accuracy, due to the advantages in data mining of CNN, good time series data fitting ability of LSTM, and excellent optimization ability of SSA.

Keywords

• Short-term power load forecasting
• sparrow search algorithm
• convolutional neural network
• long short-term memory network