Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Ariana Moncada, Walter Richardson and Rolando Vega-Avila
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Ariana Moncada: Department of Mathematics, University of Texas at San Antonio, San Antonio, TX 78249, USA
Walter Richardson: Department of Mathematics, University of Texas at San Antonio, San Antonio, TX 78249, USA
Rolando Vega-Avila: CPS Energy, San Antonio, TX 78205, USA

Energies, 2018, vol. 11, issue 8, 1-16

Abstract: Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera. Reconfigurable for different operational environments, it has been deployed at the National Renewable Energy Laboratory (NREL), Joint Base San Antonio, and two locations in the Canary Islands. The original design used optical flow to extrapolate cloud positions, followed by ray-tracing to predict shadow locations on solar panels. The latter problem is mathematically ill-posed. This paper details an alternative strategy that uses artificial intelligence (AI) to forecast irradiance directly from an extracted subimage surrounding the sun. Several different AI models are compared including Deep Learning and Gradient Boosted Trees. Results and error metrics are presented for a total of 147 days of NREL data collected during the period from October 2015 to May 2016.

Keywords: solar irradiance forecasting; all-sky imaging; optical flow; artificial intelligence; deep learning; decision tree learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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