User-Side Load Forecasting Based on Multi-Neural Network Methods

With the increasing global energy consumption and the great concern for environmental protection and sustainable energy development, power load forecasting has become one of the key research areas in power systems. In particular, customer-side load forecasting, which not only affects the operation and scheduling of the power system, but also plays a crucial role in the planning, dispatch optimization, energy saving and emission reduction of smart grids, as well as the analysis of user behavior. Traditional load forecasting methods are often incompetent in the face of large-scale, nonlinear data. For this reason, this study proposes a customer-side load forecasting model based on a multi-neural network approach, which significantly improves the accuracy and robustness of the prediction by integrating several different types of neural networks, such as BP neural network, RBF neural network and wavelet neural network. The experimental results show that the proposed model can effectively handle complex load forecasting problems, especially in the processing of nonlinear features in large-scale user data, which has significant advantages. The model provides effective technical support for optimal scheduling of power systems, smart grid management, and the realization of energy saving and emission reduction goals.