In order to improve the forecasting accuracy and reliability of photovoltaic power generation

a photovoltaic power generation probability density forecasting model based on similar day and quantile regression forests(QRF) is proposed. The measured data of a photovoltaic power station are selected as the research object. On the basis of classifying the original data of the photovoltaic power generation according to different weather types

similar days are selected by two eigenvectors(temperature and wind speed). Moreover

the BP neural network(BPNN) model

support vector machine(SVM) model and QRF forecasting model are established for the historical data of similar days. The results show that

on sunny days

the forecasting results of different models can better track the change trend of true values

and the QRF model's result is closer to the true value during the period of photovoltaic power reduction from 13:00 to 16:00. On cloudy days

the forecasting errors of the above three models are relatively large from 9:00 to 12:00. In rainy days

during the sudden change of photovoltaic power generation from 14:00 to 16:00

the BPNN model has the largest forecasting error

and the SVM model's forecasting result is closer to the true value than the QRF model

but the forecasting error of the SVM model increases from 10:00 to 12:00. The QRF model has better prediction performance for different weather types among different models.