Accurate forecasting of solar irradiance is essential for effective management of power systems with significant photovoltaic generation. Machine learning algorithms, which leverage historical data and patterns to make predictions, play a crucial role in this task. One key aspect is the use of ensemble models that combine the predictions of multiple algorithms to improve forecast accuracy and reliability. In this study, ensemble models are utilized to enhance the forecasting performance by aggregating the predictions of different algorithms. Moreover, the paper proposes an ensemble feature selection method, which involves identifying the most relevant input parameters and their related past observations. This approach aims to optimize the input features used by the machine learning algorithms, ensuring that only the most pertinent information is considered for accurate solar irradiance forecasts. By leveraging the strengths of multiple algorithms and selecting the most informative features, the ensemble approach offers a robust framework for improving the
accuracy of solar irradiance predictions. The performance of several machine learning algorithms, including ensemble models, is compared for solar irradiance forecasting on days with different weather patterns using endogenous and exogenous inputs. The algorithms considered are AdaBoost, SVR, RF, XGBT, CatBoost, VOA, and VOWA. The proposed ensemble feature selection relies on the RF, IM, and Relief algorithms. The forecast accuracy is evaluated based on several metrics using a real database of the city of Salvador, Brazil. Different weather forecasts are considered: 1 hour, 2 hours, 3 hours, 6 hours, 9 hours, and 12 hours in advance. Numerical results show that the proposed ensemble feature selection improves forecast accuracy, and that the VOWA model selected with the best-performing algorithms presents forecasts with
higher accuracy than the other algorithms at different forecast time horizons. This research demonstrates the effectiveness of ensemble models and feature selection techniques in enhancing solar irradiance forecasting, providing valuable insights for efficient power system management.
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