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Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms
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DOI：10.3390/su15107943.png)
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Abstract
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This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R-2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.
Keywords:
clustering
ensemble voting
feature selection
machine learning
solar irradiation forecasting
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3.3
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10.7W
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28.4W
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