Mid-Term Power Prediction Study of Photovoltaic Based on GA-WNN Model
To solve the problem of reduced prediction accuracy due to small photovoltaic medium-term power prediction results in the presence of power limitation in photovoltaic power generation,a wavelet neural network(WNN)prediction model optimized by genetic algorithm(GA)based on photovoltaic available power is proposed.GA-WNN model establishes a WNN photovoltaic medium-term prediction training model by covering a variety of weather types such as sunny,rainy,and cloudy days within the similar dates of the prediction day,recognizing the power limitation situation through the fuzzy C-mean clustering algorithm,and taking the photovoltaic available power as the training target.GA-WNN model takes the photovoltaic numerical weather forecast obtained on the forecast day as input,and after training,it can directly predict the photovoltaic medium-term power for the next 1～10 days.It is validated by the actual operation data of a photovoltaic operating power station in Xinjiang,and the prediction accuracy reaches more than 96% .The application of GA to the WNN prediction model can significantly improve the medium-term photovoltaic power prediction accuracy.
PhotovoltaicMedium-term power predictionGenetic algorithm(GA)Wavelet neural network(WNN)Available powerFuzzy C-mean clustering
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