Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

Brian Hodge, Cong Feng, Mingjian Cui, Meredith Lee, Jie Zhang, Siyuan Lu, Hendrik Hamann

Research output: Contribution to conferencePaperpeer-review

11 Scopus Citations

Abstract

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern is determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.

Original languageAmerican English
Pages1-5
Number of pages5
DOIs
StatePublished - 29 Jan 2018
Event2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States
Duration: 16 Jul 201720 Jul 2017

Conference

Conference2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Country/TerritoryUnited States
CityChicago
Period16/07/1720/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

NREL Publication Number

  • NREL/CP-5D00-71573

Keywords

  • Classification
  • Pattern recognition
  • Sky imaging
  • Solar forecasting
  • Support vector machine

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