Short-term load forecasting based on BP neural network for electric power system

Short-term load forecasting for electric power system directly influences the economic benefit of electric power enterprises.The short-term load forecasting is established based on BP neural network for electric power system with the power load and meteorological data including weather,temperature and humidity before the forecasting day as input,and the power load at the forecasting day as output.The history load data is adopted as training sample to train BP network forecasting model.Then the trained network is used in short-term load forecasting.Besides,the real history data is used in the short-term load forecasting for a region in Xinjiang.The result shows that the forecasting is close to the practice with an average forecasting accuracy 98.45% of 96 sampling points in one day.