Real-time prediction intervals for intra-hour DNI forecasts

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

Renewable Energy, 2015, vol. 83, issue C, 234-244

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 20 min. 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.

Keywords: Solar forecasting; Prediction intervals; Sky imaging; Support vector machines; Artificial neural networks (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:83:y:2015:i:c:p:234-244

DOI: 10.1016/j.renene.2015.04.022

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