A novel photovoltaic power output forecasting method based on weather type clustering and wavelet support vector machines regression

Due to the strong randomness and intermittency of photovoltaic (PV) power output, accurate PV power output forecast becomes more and more important for system reliability, meanwhile it can promote large-scale PV deployment. In this paper, a novel PV power output forecast model based upon weather type clustering and support vector machines (SVM) regression is proposed. Firstly, on the basis of calculated average historical PV power output of each weather type, expectation maximization (EM) algorithm is adopted to cluster weather types into some categories. Secondly, based on clustering results and weather information collected from authoritative meteorological administration, the input samples are selected to better reflect weather characteristics of the forecasting day. Finally, for certain weather type, a wavelet SVM regression approach is adopted to forecast PV power output. Extensive experimental results demonstrate that the proposed model for PV power output forecasting has a high forecasting accuracy.