Short-term PV Generation Forecasting Based On Weather Type Clustering And Improved GPR Model
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 built; 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.
Gaussian Process Regression weather type index short-term forecasting linear weight reduction PSO hyperparameter
L. Chong J. Rong D. Wenqiang S.Weicheng M. Xiping
Xian university of technology,Shaanxi. Gansu Electric Power Research Institute,Gansu
国际会议
西安
英文
1-5
2016-09-01（万方平台首次上网日期，不代表论文的发表时间）