Bi-model short-term solar irradiance prediction using support vector regressors

Hsu Yung Cheng, Chih Chang Yu, Sian Jing Lin

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

This paper proposes an accurate short-term solar irradiance prediction scheme via support vector regression. Utilizing clearness index conversion and appropriate features, the support vector regression models are able to output satisfying prediction results. The prediction results are further improved by the proposed ramp-down event forecasting and solar irradiance refinement procedures. With the help of all-sky image analysis, two separated regression models are constructed based on the cloud obstruction conditions near the solar disk. With bi-model prediction, the behavior of the changing irradiance can be captured more accurately. Moreover, if a ramp-down event is forecasted, the predicted irradiance is corrected based on the cloud cover ratio in the area near the sun. The experiments have shown that the proposed method can effectively improve the prediction accuracy on a highly challenging dataset.

Original languageEnglish
Pages (from-to)121-127
Number of pages7
JournalEnergy
Volume70
DOIs
StatePublished - 1 Jun 2014

Keywords

  • All-sky image
  • Clearness index
  • Ramp-down events
  • Solar irradiance prediction
  • Support vector regression

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