Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting
A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contai...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/8/1979 |
| _version_ | 1797570444614696960 |
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| author | Sue Ellen Haupt Tyler C. McCandless Susan Dettling Stefano Alessandrini Jared A. Lee Seth Linden William Petzke Thomas Brummet Nhi Nguyen Branko Kosović Gerry Wiener Tahani Hussain Majed Al-Rasheedi |
| author_facet | Sue Ellen Haupt Tyler C. McCandless Susan Dettling Stefano Alessandrini Jared A. Lee Seth Linden William Petzke Thomas Brummet Nhi Nguyen Branko Kosović Gerry Wiener Tahani Hussain Majed Al-Rasheedi |
| author_sort | Sue Ellen Haupt |
| collection | DOAJ |
| description | A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy. |
| first_indexed | 2024-03-10T20:25:41Z |
| format | Article |
| id | doaj.art-ef3b2c22c909482f905fefa91e9f68fb |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:25:41Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ef3b2c22c909482f905fefa91e9f68fb2023-11-19T21:51:56ZengMDPI AGEnergies1996-10732020-04-01138197910.3390/en13081979Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy ForecastingSue Ellen Haupt0Tyler C. McCandless1Susan Dettling2Stefano Alessandrini3Jared A. Lee4Seth Linden5William Petzke6Thomas Brummet7Nhi Nguyen8Branko Kosović9Gerry Wiener10Tahani Hussain11Majed Al-Rasheedi12Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USAEnergy and Building Research Center, Kuwait Institute for Scientific Research, Kuwait City 13109, KuwaitEnergy and Building Research Center, Kuwait Institute for Scientific Research, Kuwait City 13109, KuwaitA modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy.https://www.mdpi.com/1996-1073/13/8/1979wind energysolar energyrenewable energy forecastingartificial intelligencemachine learning |
| spellingShingle | Sue Ellen Haupt Tyler C. McCandless Susan Dettling Stefano Alessandrini Jared A. Lee Seth Linden William Petzke Thomas Brummet Nhi Nguyen Branko Kosović Gerry Wiener Tahani Hussain Majed Al-Rasheedi Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting Energies wind energy solar energy renewable energy forecasting artificial intelligence machine learning |
| title | Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting |
| title_full | Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting |
| title_fullStr | Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting |
| title_full_unstemmed | Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting |
| title_short | Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting |
| title_sort | combining artificial intelligence with physics based methods for probabilistic renewable energy forecasting |
| topic | wind energy solar energy renewable energy forecasting artificial intelligence machine learning |
| url | https://www.mdpi.com/1996-1073/13/8/1979 |
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