Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning
Solar energy production using photovoltaic (PV) systems is increasingly popular as a source of renewable energy for numerous applications. However, there is a main challenge with solar energy, namely, the unpredictability of its energy output. Therefore, accurate short-term predicting of the power o...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135564 |
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| author | Caleb Harrison Phadungsak Tubuntoeng Xudong Liu |
| author_facet | Caleb Harrison Phadungsak Tubuntoeng Xudong Liu |
| author_sort | Caleb Harrison |
| collection | DOAJ |
| description | Solar energy production using photovoltaic (PV) systems is increasingly popular as a source of renewable energy for numerous applications. However, there is a main challenge with solar energy, namely, the unpredictability of its energy output. Therefore, accurate short-term predicting of the power output for PV systems is essential for effective decision making in power grid management. To this end, this paper focuses on training selected machine learning models, both traditional regression models and deep recurrent neural networks, to accurately predict solar energy output on meteorological time-series data from the Alice Springs solar farm in Australia. These machine learning models include linear regression, gated recurrent unit, recurrent neural network, long short-term memory, and random forest regression. The results of these tests showed that simple ensemble methods can outperform powerful single models and that hyperparameter tuning can greatly improve the performance of a model |
| format | Article |
| id | doaj-art-5253609c03044850b7f82f81f699e708 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-5253609c03044850b7f82f81f699e7082025-08-20T01:52:19ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13556471943Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine LearningCaleb Harrison0Phadungsak TubuntoengXudong LiuUniversity of North FloridaSolar energy production using photovoltaic (PV) systems is increasingly popular as a source of renewable energy for numerous applications. However, there is a main challenge with solar energy, namely, the unpredictability of its energy output. Therefore, accurate short-term predicting of the power output for PV systems is essential for effective decision making in power grid management. To this end, this paper focuses on training selected machine learning models, both traditional regression models and deep recurrent neural networks, to accurately predict solar energy output on meteorological time-series data from the Alice Springs solar farm in Australia. These machine learning models include linear regression, gated recurrent unit, recurrent neural network, long short-term memory, and random forest regression. The results of these tests showed that simple ensemble methods can outperform powerful single models and that hyperparameter tuning can greatly improve the performance of a modelhttps://journals.flvc.org/FLAIRS/article/view/135564 |
| spellingShingle | Caleb Harrison Phadungsak Tubuntoeng Xudong Liu Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning |
| title_full | Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning |
| title_fullStr | Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning |
| title_full_unstemmed | Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning |
| title_short | Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning |
| title_sort | predicting solar energy output on meteorological time series data using machine learning |
| url | https://journals.flvc.org/FLAIRS/article/view/135564 |
| work_keys_str_mv | AT calebharrison predictingsolarenergyoutputonmeteorologicaltimeseriesdatausingmachinelearning AT phadungsaktubuntoeng predictingsolarenergyoutputonmeteorologicaltimeseriesdatausingmachinelearning AT xudongliu predictingsolarenergyoutputonmeteorologicaltimeseriesdatausingmachinelearning |