Real-time prediction intervals for intra-hour DNI forecasts

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

55 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.

Original languageEnglish
Pages (from-to)234-244
Number of pages11
JournalRenewable Energy
Volume83
DOIs
Publication statusPublished - 1 Nov 2015

Keywords

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

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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