Predicting Solar PV Power Under Real-World Conditions: A Comparative Evaluation of Interpretable Machine Learning Models

Solar power generation prediction plays a vital role in improving the stability of the power grid, energy management, and the integration of renewable energy with the existing power grid to achieve the required objectives. Due to its inherent variability and climate dependent nature, solar photovoltaic output poses a huge challenge for grid operators and energy planners such as the capacity of the energy producers to predict output with high accuracy and the capacity of the grid to adjust to the nonlinear relationship between environmental factors and PV output. In this study, the authors compare four popular machine learning models: Linear Regression, Decision Tree, Random Forest and Extreme Gradient Boosting (XGBoost) to predict solar power generation from a publicly available data set from the solar plant in South Asia. The data set consists of 68,778 samples with attributes such as irradiation, ambient temperature, module temperature and the target is the AC power. Experiments conducted in the same way for all models: Train/Test split 80/20, no shuffling of time series. The performance of the models was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The experimental results prove that the model of Random Forest has the best results with an MAE of 34.700, RMSE of 53.261, and an R² of 0.976, which is quite close to the results of XGBoost with an R² of 0.974. As expected, with the basic physics of solar power generation, feature importance analysis showed that the irradiation was the most important predictor accounting for about 97% of the model’s predictive power. The irradiation and AC output power were correlated and found to be highly positive (0.98). The results indicate that the ensemble methods are not only accurate in predicting the results but also computational efficient and easy to interpret, thus they are suitable for use in resource-limited settings, like developing countries.