Research on Household Electricity Prediction Based on Improved Neural Network

In order to solve the problems of gradient vanishing and exploding in the prediction process of complex and multi temporal characteristic electricity load data using traditional neural networks, and to overcome the shortcomings of poor performance of a single prediction model, this paper proposes an improved neural network prediction method that integrates self-attention mechanism. On the one hand, the long-term and short-term dependency characteristics and multi period characteristics are fully considered in feature data extraction, and the global correlations are extracted by the multivariate household electricity time series modeling. On the other hand, various neural network features such as CNN and LSTM are integrated into the prediction model and self attention mechanism is further applied to highlight the load impact factor and improve the prediction performance of the model. Finally, based on real residential electricity consumption data, the accuracy and stability of the proposed control strategy were verified by comparing it with traditional neural network prediction methods.