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- Title
Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms.
- Authors
Solano, Edna S.; Affonso, Carolina M.
- Abstract
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 R2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.
- Subjects
MACHINE learning; VOTING machines; FEATURE selection; RANDOM forest algorithms; DATABASES; BOOSTING algorithms
- Publication
Sustainability (2071-1050), 2023, Vol 15, Issue 10, p7943
- ISSN
2071-1050
- Publication type
Article
- DOI
10.3390/su15107943