Short-term Prediction of Photovoltaic Power based on DsRNN and Multi-source Meteorological Data
Aiming at the shortcomings that the traditional recurrent neural network(RNN)will have gra-dient explosion for long-term use and easily ignore important time series information when processing long-term sequences,this paper proposed a double selection recurrent neural network(DsRNN)combined with attention mechanism,which was oriented to the short-term photovoltaic power prediction model.First,the meteorological impact factor data was introduced and corrected according to the correlation size,and a new data set was established by changing the original single input source;then,the attention mech-anism was integrated to extract the time series features of photovoltaic power,and mine the deep relation-ship between the data;finally,a more effective and accurate short-term power prediction for distributed photovoltaic power generation was realized.The simulation results show that the input of meteorological data and the use of the DsRNN photovoltaic power prediction model can complete the prediction task with higher precision and the error is smaller.
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