Real-time forecasting of solar irradiance ramps with smart image processing

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

101 Citations (Scopus)


We develop a standalone, real-time solar forecasting computational platform to predict one minute averaged solar irradiance ramps ten minutes in advance. This platform integrates cloud tracking techniques using a low-cost fisheye network camera and artificial neural network (ANN) algorithms, where the former is used to introduce exogenous inputs and the latter is used to predict solar irradiance ramps. We train and validate the forecasting methodology with measured irradiance and sky imaging data collected for a six-month period, and apply it operationally to forecast both global horizontal irradiance and direct normal irradiance at two separate locations characterized by different micro-climates (coastal and continental) in California. The performance of the operational forecasts is assessed in terms of common statistical metrics, and also in terms of three proposed ramp metrics, used to assess the quality of ramp predictions. Results show that the forecasting platform proposed in this work outperforms the reference persistence model for both locations.

Original languageEnglish
Pages (from-to)91-104
Number of pages14
JournalSolar Energy
Publication statusPublished - 1 Apr 2015


  • Cloud transport
  • Ramp forecasts
  • Sky imaging
  • Smart forecasts
  • Solar forecasting

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)


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