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A Smart Image-Based Cloud Detection System for Intrahour Solar Irradiance Forecasts

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  • 1 Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, and Center for Renewable Resource Integration, and Center for Energy Research, University of California, San Diego, La Jolla, California
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Abstract

This study proposes an automatic smart adaptive cloud identification (SACI) system for sky imagery and solar irradiance forecast. The system is deployed using off-the-shelf fish-eye cameras that offer substantial advantages in terms of cost when compared to industry-standard sky imagers. SACI uses a smart image categorization (SIC) algorithm that combines the sky images and solar irradiance measurements to classify sky conditions into three categories: clear, overcast, and partly cloudy. A cloud detection scheme, optimized for each image category, is used to quantify cloud cover from the sky images. SACI is optimized and validated against manually annotated images. Results show that SACI achieves overall classification accuracy higher than 90% and outperforms reference cloud detection methods. Cloud cover retrieved by SACI is used as an input for an artificial neural network (ANN) model that predicts 1-min average global horizontal irradiance (GHI), 5-, 10-, and 15-min ahead of time. The performance of the ANN forecasting model is assessed in terms of common error statistics (mean bias and root-mean-square error) and in terms of forecasting skill over persistence. The model proposed in this work achieves forecasting skills above 14%, 18%, and 19% over the persistence forecast for 5-, 10-, and 15-min forecasts, respectively.

Corresponding author address: Carlos F. M. Coimbra, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093. E-mail: ccoimbra@ucsd.edu

Abstract

This study proposes an automatic smart adaptive cloud identification (SACI) system for sky imagery and solar irradiance forecast. The system is deployed using off-the-shelf fish-eye cameras that offer substantial advantages in terms of cost when compared to industry-standard sky imagers. SACI uses a smart image categorization (SIC) algorithm that combines the sky images and solar irradiance measurements to classify sky conditions into three categories: clear, overcast, and partly cloudy. A cloud detection scheme, optimized for each image category, is used to quantify cloud cover from the sky images. SACI is optimized and validated against manually annotated images. Results show that SACI achieves overall classification accuracy higher than 90% and outperforms reference cloud detection methods. Cloud cover retrieved by SACI is used as an input for an artificial neural network (ANN) model that predicts 1-min average global horizontal irradiance (GHI), 5-, 10-, and 15-min ahead of time. The performance of the ANN forecasting model is assessed in terms of common error statistics (mean bias and root-mean-square error) and in terms of forecasting skill over persistence. The model proposed in this work achieves forecasting skills above 14%, 18%, and 19% over the persistence forecast for 5-, 10-, and 15-min forecasts, respectively.

Corresponding author address: Carlos F. M. Coimbra, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093. E-mail: ccoimbra@ucsd.edu
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