Back Propagation Neural Network for Short-term Electricity Load Forecasting with Weather Features

In this paper, we expand previous work and present an accurate electricity load forecasting algorithm with back propagation neural networks. It contributes to short-term electricity load forecast methodology with neural network with weather feature such as max centigrade, min centigrade and weather types. The original electricity load is from shanghai district, which is composed of original every 5 minutes load records. Through the data transform the every hour data average 12 original records and weather feature become double value. After training and simulation, the prediction errors fit for the application needs. The algorithm besides other algorithms has been used in electricity load forecasting software. Many results confirm that the proposed method is capable of forecasting load efficiently.