Power Generation Forecasting Methods of Photovoltaic Power Plants Based on Genetic Wavelet Neural Network Method
Aiming at the problems of inaccurate prediction of photovoltaic power plant power generation and fluctuations in prediction results caused by meteorological factors,this paper proposes a genetic wavelet neural network method is proposed for predicting the power generation of photovoltaic power plants.Firstly,using the structure of the back propagation(BP)neural network as the framework,the wavelet basis function is selected as the hidden layer.The network connection weight,wavelet function scaling factor,and wavelet function translation factor are considered as genetic individuals,and the optimal initial parameters of the network are obtained through individual optimization using genetic algorithms.Then,the optimized network is used to perform simulation prediction and the simulation data is analyzed.Finally,the prediction results are compared with actual power generation to evaluate errors and reliability of the prediction model.Experimental analysis shows that the genetic wavelet neural network prediction model has smaller errors and higher prediction accuracy,making it suitable for accurate prediction of power generation in photovoltaic power plants.
photovoltaic power stationpower generation predictiongenetic algorithmwavelet neural network
万方数据
维普
