Optimized Support Vector Regression-Based Model for Solar Power Generation Forecasting on the Basis of Online Weather Reports

Increasing the forecasting accuracy of photovoltaic (PV)-generated power is currently an important topic, particularly in the maintenance of the stability and reliability of modern electric grid systems. In this study, a model based on a particle swarm optimization (PSO)-optimized support vector regression (SVR) is proposed for the accurate forecasting of PV output power. In the process, an SVR-based model is established based on the most influential historical experimental data collected from an actual PV power station. A PSO-based algorithm is adapted for the selection of dominant SVR-based model parameters and improvement of performance. Moreover, a novel data preparation algorithm is developed for the preparation of a solar irradiance pattern on the basis of weather conditions and the percentages of cloud…