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Abstract

Artificial neural networks are one of the main models for predicting PV power. The accuracy of input data in artificial neural networks is the main factor affecting the accuracy of PV power prediction. This article uses accurate weather data from historical measurements and uses the grey model GM(1,1) to predict the current weather data. When using the grey model, multiple lengths of historical data sequences are selected for prediction, and the average relative error is used to evaluate the fitting effect on historical data. The weather data predicted by the sequence with the best fitting effect on historical data is selected. The input data of the artificial neural network is obtained by weighting the weather data predicted by the weather forecast with the weather data predicted by the grey model. The weights are dynamically adjusted based on the fitting effect of the grey model on historical data. The simulation of existing photovoltaic power station data has verified the effectiveness of the algorithm proposed in this paper.

Keywords

PV power prediction neural network grey model input data average relative error

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Neural network input data calculation method based on the grey model

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