A Hybrid Ensemble Learning Strategy for Enhancing Solar Photovoltaic Forecasting in Renewable Energy Systems

Abstract

The successful forecast of the solar photovoltaic (PV) power processing is essential in increasing grid stability, and in the ultimate inclusion of renewable energy. This paper is a research project aiming to introduce a hybrid classifier of Random Forest (RF), K-Nearest Neighbors (KNN), and Elastic Net (EN) to enhance multi-regional solar PV power forecasting using time-series. Preprocessing of Hourly PV generation data was done by taking out the temporal feature like hour, day and month to improve forecasting. To compare the proposed hybrid model with individual algorithms, the coefficient of determination (R2), mean absolute error (MAE) were used to evaluate the model. The results of the experiment indicate that the hybrid model had an R 2 of 0.91, which was higher than RF (0.88), KNN (0.82), and EN (0.79), and nearly halved the MAE, relative to single models. The results prove that the combination of multiple regression methods would lead to better prediction robustness, as well as lower variance and increase forecasting accuracy, which makes the suggested method an appropriate tool to study smart grids and sustainable energy planning.