Ultra-short-term Prediction of Photovoltaic Power Based on Dataset Distillation
Cloud is the main factor affecting the change of direct solar radiation.Due to the different transmittance of various clouds,the solar radiation of photovoltaic power station will fluctuate accordingly.In order to solve the problems of large fluctuation and large number of prediction models of photovoltaic power generation under various clouds,an ultra-short-term prediction model of photovoltaic power generation based on satellite cloud image and data set distillation is proposed.First,based on the historical cloud image above the station to be measured,the Farneback optical flow method is used to predict the cloud image.Then,the sample library of all kinds of clouds is established according to the satellite cloud classification label data,and the cloud class discriminant map is obtained by training sample library of the data set with distillation algorithm.The predicted cloud image is matched with the cloud class discriminant map to obtain the cloud class aggregation matching feature.Finally,the long short-term memory network model is established by using the above features,cloud cover feature and numerical weather forecast data to predict the ultra-short-term photovoltaic power generation.The results show that the proposed model can accurately describe the characteristics of clouds and effectively improve the prediction accuracy of photovoltaic power.
dataset distillationsatellite cloud imagescloud classificationoptical flow methodultra-short-term PV power forecast
国家科技期刊平台
NSTL
万方数据
