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Wind Power Load Forecasting Based on Incremental BP Neural Network

Wind power load forecasting is an important part of energy internet, which is the basis of rational arrange the power grid operation plan. As a kind of time series data, wind power load data has the characteristics of time series and large quantity. To overcome the deficiency that the latest information of time series has great influence on the accuracy of wind power load forecasting, this paper proposes a method of adding and deleting hidden nodes of BP neural network to realize the incremental learning of BP neural network training. By dynamically adding and deleting the number of hidden layer neurons and training the latest time series information in BP neural network, this method can improve the effect of network training and avoid the convergence of traditional BP neural network to local minimum. By comparing the wind power load forecasting results of batch training BP neural network and incremental BP neural network with real wind power load, the results show that the wind power load forecasting accuracy based on incremental BP neural network is higher.