Combining Physical Modeling with Artificial Intelligence for Solar Power Forecasting

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Modern renewable energy forecasting systems blend physical models with artificial intelligence (AI). This paper describes such a system being developed for the Shagaya Renewable Energy Park in Kuwait. This Kuwait Renewable Energy Prediction System uses both physical modeling and machine learning to produce forecasts for short-range forecasting (i.e. the next six hours) as well as for forecasts several days out. These systems work together to implement the best of our physical knowledge of atmospheric flow with actual observations at the site of interest. The system include numerical weather prediction, blending models, statistical learning models for short range prediction, and an analog ensemble to complete the forecast and quantify the uncertainty. This description includes a preliminary assessment of how each of these models performs.

Original languageEnglish
Title of host publication2020 47th IEEE Photovoltaic Specialists Conference, PVSC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2051-2053
Number of pages3
ISBN (Electronic)9781728161150
DOIs
StatePublished - Jun 14 2020
Event47th IEEE Photovoltaic Specialists Conference, PVSC 2020 - Calgary, Canada
Duration: Jun 15 2020Aug 21 2020

Publication series

NameConference Record of the IEEE Photovoltaic Specialists Conference
Volume2020-June
ISSN (Print)0160-8371

Conference

Conference47th IEEE Photovoltaic Specialists Conference, PVSC 2020
Country/TerritoryCanada
CityCalgary
Period06/15/2008/21/20

Keywords

  • artificial intelligence
  • Kuwait
  • numerical weather prediction
  • Shagaya
  • solar power forecasting

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