Multiphase Solar Photovoltaic Prediction Model Based on Season, Hierarchical <i>k</i>-Means Clustering, GRA-PCC, SVM, and Neural Network

Abstract

Solar photovoltaic (PV) has accounted for the highest percentage of power generation capacity among other renewables. However, solar PV power generation is highly variable because of different factors; therefore, accurate forecasting is critical for reliable integration into the power system. This paper proposes a multiphase solar PV prediction model that includes grouping, clustering, linking, classifying, and predicting using historical solar PV power and weather data. Seasonal variation is considered in the grouping phase, followed by hybrid hierarchical k-means clustering to enhance data division in the clustering phase. A hybrid gray relational analysis-Pearson correlation coefficient identifies significant weather factors impacting solar PV power in the linking phase. The classification phase employs a support vector machine to establish the relationship between the clusters and the relevant weather factors. Lastly, a neural network (NN) is trained to predict solar PV power. The solar PV power profiles are presented to show the variability in season and time. The simulation results of the proposed model showed relatively accurate forecasting results, including MAE of 0.408 MW, MSE of 460.51 MW, RMSE of 0.679 MW, nRMSE of 4.345%, and MRE of 2.266%. These results represent that the uncertainties of the proposed model are 6 and 12 times lower than those of the conventional methods (i.e., conventional NN and ARMAX). These results assure that the proposed model can provide more accurate solar PV power profiles for reliable power system integration.