Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Abstract

Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial f luctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data features. Subsequently, the moving average method is used for data preprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSO algorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed and accuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressing information loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According to the numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It can explore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods for the household load exhibiting significant f luctuations across different seasons.