Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting

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 and Majed Al-Rasheedi
Additional contact information
Sue Ellen Haupt: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Tyler C. McCandless: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Susan Dettling: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Stefano Alessandrini: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Jared A. Lee: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Seth Linden: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
William Petzke: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Thomas Brummet: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Nhi Nguyen: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Branko Kosović: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Gerry Wiener: Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Tahani Hussain: Energy and Building Research Center, Kuwait Institute for Scientific Research, Kuwait City 13109, Kuwait
Majed Al-Rasheedi: Energy and Building Research Center, Kuwait Institute for Scientific Research, Kuwait City 13109, Kuwait

Energies, 2020, vol. 13, issue 8, 1-23

Abstract: 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.

Keywords: wind energy; solar energy; renewable energy forecasting; artificial intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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