Research on short-term electricity price prediction in power market based on BP neural network

In the electricity market environment, accurate tariff forecasting affects the decisions of power generators, power suppliers and consumers. In order to accurately forecast electricity prices, it is necessary to analyze the factors influencing electricity prices and then select input data to analyze and forecast using forecasting models. However, with the advancement of smart grid, communication network technology and sensor technology, the scope of electric load data has grown exponentially and the complexity has increased, requiring the selection of prediction models. And neural networks can perform a large number of data inputs and have good generalization ability, so they can handle various nonlinear problems. In this paper, the forward transmission of its input signal and the feedback process of the error signal are introduced and analyzed from the principle layer of BP neural network to provide a reference model for electricity price prediction.