Improving Solar PV Prediction Performance with RF-CatBoost Ensemble: A Robust and Complementary Approach

Although solar energy is renewable, environmental conditions can make it unpredictable, which makes it difficult to maintain a consistent supply of electricity. Accurate solar forecast techniques are essential to overcome this. Therefore, an efficient model for predicting solar irradiance and PV power output is presented in this study. The suggested model is especially designed for Agartala city in Tripura, India, where the installed capacity of Grid Interactive Solar Power is extremely low. The proposed model for solar irradiance prediction involves utilizing a combination of the Random Forest and CatBoost algorithms. The model is developed using 10 years of solar data and other relevant meteorological parameters with a 1-hour interval and assessed at various stages to make long-term monthly predictions. In addition, we compared this model to other existing models for solar irradiance prediction. The study showcased the efficacy of Random Forest and CatBoost algorithms when utilized individually and as an ensemble. The results demonstrate the effectiveness of the Random Forest and CatBoost ensemble, with an impressive accuracy improvement of 6% and an R2-score of 86%. Additionally, in terms of RMSE and MAE, the model demonstrates superior performance with lower values of 83.8466 and 45.4011, respectively confirms to the proposed model's viability and suitability for long-term solar radiation and PV power predictions.& COPY; 2023 Published by Elsevier Ltd.