Short-term PV generation forecasting based on weather type clustering and improved GPR model

摘要

A combined prediction method based on weather type index and Gaussian Process Regression(GPR) improved by linear weight reduction PSO algorithm was proposed to deal with the problem of forecasting of short-term PV generation. Firstly, the samples, according to the weather type index, was built, and then three types were classified based on daily mean temperature and humanity; the different GPR model, considering different highly correction factors, which measuring combined covariance functions and taking LinW-PSO to optimize the hyperparameters of model, were bui The corresponding computer code was programmed in MATLAB. Seen from simulation results of forecasting of the PV station in XinJiang, the given classification method and LinW-PSO-GPR combination predicting method enable the rate of convergence and the accuracy of results improved.