Real-time prediction intervals for intra-hour DNI forecasts

Yinghao Chu, Mengying Li, Hugo T.C. Pedro, Carlos F.M. Coimbra*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

100 Citations (Scopus)

Abstract

We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. We validate the proposed model using six-month of measured irradiance and sky image data, and apply it to construct operational PI forecasts in real-time at the same observatory. In the real-time scenario, the hybrid model significantly outperforms the reference persistence model and provides high performance PIs regardless of forecast horizon and weather condition. © 2015 Elsevier Ltd.
Original languageEnglish
Pages (from-to)234-244
JournalRenewable Energy
Volume83
DOIs
Publication statusPublished - 1 Nov 2015
Externally publishedYes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

  • Artificial neural networks
  • Prediction intervals
  • Sky imaging
  • Solar forecasting
  • Support vector machines

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