A power load forecasting model based on a combined neural network

Abstract

The supply of electric power is vital for the daily lives of people, industrial production, and business services. At present, although enough electric power can be supplied to meet the power demand, there are still some challenges, especially in terms of long-distance power transmissions and long-term power storage. Consequently, if the power production capacity exceeds the immediate consumption requirements, i.e., the produced electric power cannot be consumed in a short period, and much electric power could be wasted. Evidently, to minimize the wastage of electric power, it is necessary to properly plan power production by accurately forecasting the future power load. Therefore, a preferable power load forecasting algorithm is crucial for the planning of power production. This paper proposes a novel deep learning model for the purpose of power load forecasting, termed the SSA-CNN-LSTM-ATT model, which combines the CNN-LSTM model with SSA optimization and attention mechanisms. In this model, the CNN module extracts the features from the sequential data, and then the features are passed to the LSTM module for modeling and capturing the long-term dependencies hidden in the sequences. Subsequently, an attention layer is employed to measure the importance of different features. Finally, the output is obtained through a fully connected layer, yielding the forecasting results of the power load. Extensive experiments have been conducted on a real-world dataset, and the metric R2 can reach 0.998, indicating that our proposed model can accurately forecast the power load.