With the infiltration of new energy into the power grid,the ambiguity in the operation of the power system increases.Therefore,in order to ensure the stable operation of the power system network,accurate and effective prediction of the new energy generation is crucial.This paper uses a multi-layer feedforward artificial neural network(FF-ANN)model to train the new energy generation prediction dataset.The research involves two steps,namely training and prediction.During the training process,a long and short memory learning algorithm is used to optimize the parameters of the FF-ANN.In order to predict the power of the new energy sources,a new prediction techniqueisproposed,which is called the weighted least squares error correlation(WLSEC)and has been implemented on the C++platform.The performance of this model has been tested in actual operation by taking into account one year of historical data with hourly resolution.The hourly average absolute percentage error(MAPE)of predicting the new energy is 7.32%,while the backpropagation neural network(BPNN)is 9%,this clearly demonstrates the effectiveness of the proposed model in predicting the new energy generation.
Neural networkNew energyPower predictionData training
维普
万方数据
