Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting
| Autor: | William Petzke, Tyler McCandless, Jared A. Lee, Stefano Alessandrini, Seth Linden, Majed Al-Rasheedi, Thomas Brummet, Nhi Nguyen, Sue Ellen Haupt, Susan Dettling, Gerry Wiener, Tahani Hussain, Branko Kosovic |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
010504 meteorology & atmospheric sciences Computer science 020209 energy solar energy Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology 01 natural sciences wind energy renewable energy forecasting artificial intelligence machine learning Component (UML) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) 0105 earth and related environmental sciences Relative value Wind power lcsh:T Renewable Energy Sustainability and the Environment business.industry Photovoltaic system Probabilistic logic Solar energy Grid Renewable energy Artificial intelligence business Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 8; Pages: 1979 Energies, Vol 13, Iss 1979, p 1979 (2020) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13081979 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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