Short-Term Prediction of Photovoltaic Power Based on Meteorological Factors
Photovoltaic power generation has the characteristics of randomness and fluctuation,which affects the matching with power system to some extent.In order to solve this problem,people put forward to predict the photovoltaic power and improve the accuracy is the key problem of photovoltaic prediction.Long-term memory(LSTM)has a good processing effect in time series.Based on the meteorological factors affecting photovoltaic power generation,this paper constructs a deep learning model based on LSTM to predict photovoltaic power generation.Then the dimension of meteorological factors affecting photovoltaic power generation is reduced by isolated forest algorithm,and the main factors are selected as features.The prediction results show that the precision of the model after dimension reduction is higher than that before dimension reduction.
photovoltaic power generationpredictiondimension reduction
NETL
NSTL
万方数据
