Short-Term Load Forecasting with Elman Neural Network Based on Body Amenity Indicator and Innovation

Power system short-term load forecasting have a significant role in the power system planning and reliable operation. According to the deficiency of time variation and easy to fall into the local least value in feed forward neural networks, a forecasting Model is provided by using Elman feedback neural network. Hourly weather factors can improve the prediction precision. In order to achieve the balance of the least input neurons and prediction precision, the regionality human body amenity indicator is used as the input of meteorological factors. Meanwhile, in order to take full advantage of the historical load data, improve the prediction precision, the concept of innovation is introduced and the load data of forecast base day is included in the input range. The model is verified by using the actual data, the results show that the prediction results is accurate and the model in practical and effective.