Short-term load forecasting with comprehensive weather factors based on improved Elman neural network
Fang Ge-fei · Power System Protection and Control · 2012
Since the regional power load is significantly affected by weather factors, a method considering weather factors is proposed. This method uses comprehensive weather factors, namely the human body amenity indicator and THI (temperature and humidity index), as inputs, which overcomes the disadvantages such as too many inputs and long forecasting time when weather factors are direct inputs. Besides, in view of the relatively low dynamic performance of BP neural network, a short-term load forecasting model based on Elman neural network is provided. Furthermore, improvements on excitation function and the structures of network have been made. The improved model considers the grid’s dynamical performance, decreases the number of inputs and enhances the adaptability of the load forecasting model. This paper has verified the method and model using the data of Hangzhou. The results show that the method and model can significantly increase the precision of prediction, thus the method and model are practical and effective.