Short-Term Load Forecasting with WNN Based on Body Amenity Indicator

Power system short-term load forecasting have a significant role in the power system planning and reliable operation. This paper compared the result of two prediction models BP neural network and wavelet neural network (WNN). According to comparison results, WNN model in the case of the same number of nodes have better predictive accuracy. 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.