A method for forecasting alpine area load based on artificial neural network model

Abstract

In order to realize the modernization of power grid management, mid-term power load forecasting has become an important research in power dispatching. Accurate mid-term load forecasting is of great significance to future power system dispatching and safe operation, and is a prerequisite for safe and economical operation of power systems. Based on error back propagation (BP) neural network has the advantages of generalization ability, self-learning ability and self-adapting ability, and simple local calculation, this paper proposes to use the BP neural network model to forecast the power load in the alpine region. Firstly, the historical load data and meteorological data of a certain area in Harbin are studied, analyzed and screened, and they are used as the input data of the network, a mid-term load forecasting model based on BP neural network is established, and then the model is simulated and improved through simulation to predict changes in the future development of electric load demand. The prediction data results and images show that the medium-term load prediction accuracy and training speed based on the BP neural network can reach the target, and the performance is good.