Artificial Intelligence Assisted Solar Energy Forecasting by Explainability Approaches with LIME and SHAP

Authors : Ali Öter, Betül Ersöz

Pages : 205-212

View : 64 | Download : 59

Publication Date : 2025-05-01

Article Type : Research Paper

Abstract :Integrating renewable energy sources with new technologies such as artificial intelligence (AI) is important to balance energy supply and demand. The predictability of variable energy sources, such as solar energy, plays an important role in maintaining the stability and efficiency of power grids. This study examines the use of various algorithms in AI applications within renewable energy systems. The study critically evaluates existing methods and proposes an innovative approach for AI prediction in solar energy systems using advanced machine learning techniques. It focuses on the effectiveness of MLP, Ridge, and RF algorithms in forecasting Direct Current (DC). The results showed that the RF algorithm achieved the highest R² value (0.9999) and the lowest error RMSE (0.0024) and MAE (0.0006) measurements to demonstrate the superior ability of the models to explain variance in the data and make accurate predictions. In addition, the model developed with SHAP and LIME explainable AI algorithms is interpreted.
Keywords : Güneş enerjisi, Makine öğrenimi, Açıklanabilir YZ, SHAP, LIME, Yenilenebilir enerji