Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection
Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble fe...
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Format: | Article |
Language: | English |
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MDPI AG
2022-09-01
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Series: | Energies |
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Online Access: | https://www.mdpi.com/1996-1073/15/19/7049 |
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author | Edna S. Solano Payman Dehghanian Carolina M. Affonso |
author_facet | Edna S. Solano Payman Dehghanian Carolina M. Affonso |
author_sort | Edna S. Solano |
collection | DOAJ |
description | Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons. |
first_indexed | 2024-03-09T21:48:25Z |
format | Article |
id | doaj.art-ddf307e1a440412f98ede3f7a8409e89 |
institution | Directory Open Access Journal |
issn | 1996-1073 |
language | English |
last_indexed | 2024-03-09T21:48:25Z |
publishDate | 2022-09-01 |
publisher | MDPI AG |
record_format | Article |
series | Energies |
spelling | doaj.art-ddf307e1a440412f98ede3f7a8409e892023-11-23T20:12:16ZengMDPI AGEnergies1996-10732022-09-011519704910.3390/en15197049Solar Radiation Forecasting Using Machine Learning and Ensemble Feature SelectionEdna S. Solano0Payman Dehghanian1Carolina M. Affonso2Faculty of Electrical Engineering, Federal University of Para, Belem 66075-110, PA, BrazilDepartment of Electrical and Computer Engineering, The George Washington University, Washington, DC 20052, USAFaculty of Electrical Engineering, Federal University of Para, Belem 66075-110, PA, BrazilAccurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons.https://www.mdpi.com/1996-1073/15/19/7049ensemble feature selectionmachine learningphotovoltaic generationsolar radiation forecasting |
spellingShingle | Edna S. Solano Payman Dehghanian Carolina M. Affonso Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection Energies ensemble feature selection machine learning photovoltaic generation solar radiation forecasting |
title | Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection |
title_full | Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection |
title_fullStr | Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection |
title_full_unstemmed | Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection |
title_short | Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection |
title_sort | solar radiation forecasting using machine learning and ensemble feature selection |
topic | ensemble feature selection machine learning photovoltaic generation solar radiation forecasting |
url | https://www.mdpi.com/1996-1073/15/19/7049 |
work_keys_str_mv | AT ednassolano solarradiationforecastingusingmachinelearningandensemblefeatureselection AT paymandehghanian solarradiationforecastingusingmachinelearningandensemblefeatureselection AT carolinamaffonso solarradiationforecastingusingmachinelearningandensemblefeatureselection |