A Novel Ultra-short-term Photovoltaic Power Generation Forecasting Method Based on Seasonal Autoregressive Integrated Moving Average

Abstract

This study suggests an ultra-short-term photovoltaic (PV) energy generation forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Machine (SVM). It can further increase the predictive performance of PV electricity generation electrical output and address the issue of significant fluctuation and instability of PV power generation output power. To achieve combined prediction, SVM is employed in this study to model the nonlinear SARIMA prediction residuals. In comparison to the traditional SVM method and the SARIMA method, the mixed forecasting method has a good forecasting consequence and can serve as a favorable basis for the safe operation and scheduling of the PV grid, according to the results of the simulation of actual data.