Solar Irradiation Forecasting using Genetic Algorithms
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866918155194990592 |
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| author | Gunasekaran, V. Kovi, K. K. Arja, S. Chimata, R. |
| author_facet | Gunasekaran, V. Kovi, K. K. Arja, S. Chimata, R. |
| contents | Renewable energy forecasting is attaining greater importance due to its constant increase in contribution to the electrical power grids. Solar energy is one of the most significant contributors to renewable energy and is dependent on solar irradiation. For the effective management of electrical power grids, forecasting models that predict solar irradiation, with high accuracy, are needed. In the current study, Machine Learning techniques such as Linear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization are used to forecast solar irradiation. The data used for training and validation is recorded from across three different geographical stations in the United States that are part of the SURFRAD network. A Global Horizontal Index (GHI) is predicted for the models built and compared. Genetic Algorithm Optimization is applied to XGB to further improve the accuracy of solar irradiation prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_13956 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Solar Irradiation Forecasting using Genetic Algorithms Gunasekaran, V. Kovi, K. K. Arja, S. Chimata, R. Machine Learning Neural and Evolutionary Computing 68T05 I.2.6 Renewable energy forecasting is attaining greater importance due to its constant increase in contribution to the electrical power grids. Solar energy is one of the most significant contributors to renewable energy and is dependent on solar irradiation. For the effective management of electrical power grids, forecasting models that predict solar irradiation, with high accuracy, are needed. In the current study, Machine Learning techniques such as Linear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization are used to forecast solar irradiation. The data used for training and validation is recorded from across three different geographical stations in the United States that are part of the SURFRAD network. A Global Horizontal Index (GHI) is predicted for the models built and compared. Genetic Algorithm Optimization is applied to XGB to further improve the accuracy of solar irradiation prediction. |
| title | Solar Irradiation Forecasting using Genetic Algorithms |
| topic | Machine Learning Neural and Evolutionary Computing 68T05 I.2.6 |
| url | https://arxiv.org/abs/2106.13956 |