Cubist Regression, Random Forest and Support Vector Regression for Solar Power Prediction
At a time when the energy transition is inescapable and artificial intelligence is rapidly advancing in all directions, solar renewable energy output forecasting is becoming a popular concept, especially with the availability of large data sets and the critical requirement to forecast these energies...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Renewable Energy Development Center (CDER)
2022-06-01
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| Series: | Revue des Énergies Renouvelables |
| Subjects: | |
| Online Access: | https://revue.cder.dz/index.php/rer/article/view/1040 |
| Summary: | At a time when the energy transition is inescapable and artificial intelligence is rapidly advancing in all directions, solar renewable energy output forecasting is becoming a popular concept, especially with the availability of large data sets and the critical requirement to forecast these energies, known to have a random nature. Therefore, the main goal of this study is to investigate and exploit artificial intelligence's revolutionary potential for the prediction of the electricity generated by solar photovoltaic panels. The main algorithms that will be studied in this article are cubist regression, random forest and support vector regression. This forecast is beneficial to both providers and consumers, since it will enable for more efficient use of solar renewable energy supplies, which intermittency makes their integration into the existing electrical networks a challenging task. |
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| ISSN: | 1112-2242 2716-8247 |