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

• Published in: Solar energy 2015-04, Vol.114, p.91-104
• Main Authors: Chu, Yinghao, Pedro, Hugo T.C., Li, Mengying, Coimbra, Carlos F.M.
• Format: Article
• Language: English
• Subjects: Algorithms; Cloud transport; Neural networks; Ramp forecasts; Sky imaging; Smart forecasts; Solar energy; Solar forecasting
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2015.01.024

•Total sky images captured are processed to generate cloud cover indices (CIs).•We train and optimize ANN using CIs as exogenous inputs.•The ANN is used to operationally forecast real time solar ramps.•Three ramp metrics are proposed to assess the performance of forecasts.•The ANN forecast model significantly outperforms the reference persistence model. 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.