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Atmosphere
Volume 14
Issue 11
10.3390/atmos14111635
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Article

Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions

by
Francisco Diego Vidal Bezerra
1,
Felipe Pinto Marinho
2,
Paulo Alexandre Costa Rocha
1,3,*,
Victor Oliveira Santos
3,
Jesse Van Griensven Thé
3,4 and
Bahram Gharabaghi
3
1
Department of Mechanical Engineering, Technology Center, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil
2
Department of Teleinformatics Engineering, Technology Center, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil
3
School of Engineering, University of Guelph, 50 Stone Rd. E, Guelph, ON N1G 2W1, Canada
4
Lakes Environmental, 170 Columbia St. W, Waterloo, ON N2L 3L3, Canada
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(11), 1635; https://doi.org/10.3390/atmos14111635
Submission received: 4 August 2023 / Revised: 26 September 2023 / Accepted: 27 October 2023 / Published: 31 October 2023
(This article belongs to the Special Issue Solar Irradiance and Wind Forecasting)
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Abstract

This paper proposes to analyze the performance increase in the forecasting of solar irradiance and wind speed by implementing a dynamic ensemble architecture for intra-hour horizon ranging from 10 to 60 min for a 10 min time step data. Global horizontal irradiance (GHI) and wind speed were computed using four standalone forecasting models (random forest, k-nearest neighbors, support vector regression, and elastic net) to compare their performance against two dynamic ensemble methods, windowing and arbitrating. The standalone models and the dynamic ensemble methods were evaluated using the error metrics RMSE, MAE, R2, and MAPE. This work’s findings showcased that the windowing dynamic ensemble method was the best-performing architecture when compared to the other evaluated models. For both cases of wind speed and solar irradiance forecasting, the ensemble windowing model reached the best error values in terms of RMSE for all the assessed forecasting horizons. Using this approach, the wind speed forecasting gain was 0.56% when compared with the second-best forecasting model, whereas the gain for GHI prediction was 1.96%, considering the RMSE metric. The development of an ensemble model able to provide accurate and precise estimations can be implemented in real-time forecasting applications, helping the evaluation of wind and solar farm operation.
Keywords: wind energy; solar energy; renewable energy; machine learning; forecasting ensembles wind energy; solar energy; renewable energy; machine learning; forecasting ensembles

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MDPI and ACS Style

Vidal Bezerra, F.D.; Pinto Marinho, F.; Costa Rocha, P.A.; Oliveira Santos, V.; Van Griensven Thé, J.; Gharabaghi, B. Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions. Atmosphere 2023, 14, 1635. https://doi.org/10.3390/atmos14111635

AMA Style

Vidal Bezerra FD, Pinto Marinho F, Costa Rocha PA, Oliveira Santos V, Van Griensven Thé J, Gharabaghi B. Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions. Atmosphere. 2023; 14(11):1635. https://doi.org/10.3390/atmos14111635

Chicago/Turabian Style

Vidal Bezerra, Francisco Diego, Felipe Pinto Marinho, Paulo Alexandre Costa Rocha, Victor Oliveira Santos, Jesse Van Griensven Thé, and Bahram Gharabaghi. 2023. "Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions" Atmosphere 14, no. 11: 1635. https://doi.org/10.3390/atmos14111635

APA Style

Vidal Bezerra, F. D., Pinto Marinho, F., Costa Rocha, P. A., Oliveira Santos, V., Van Griensven Thé, J., & Gharabaghi, B. (2023). Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions. Atmosphere, 14(11), 1635. https://doi.org/10.3390/atmos14111635

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