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 cover collected from online weather forecast reports. Finally, the proposed model is experimentally verified by deploying it to three different PV systems (1875Wp, 2000Wp and 2700Wp). Analytical and experimental results indicate that the proposed forecasting model ensures improved accuracy. The nRMSE of the proposed forecasting model is 2.841%. The proposed model will be effective in forecasting PV output power in existing PV systems. A guideline for the accurately forecasting of PV output power in practical applications is presented.