A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting

Walter Richardson, Hariharan Krishnaswami, Rolando Vega and Michael Cervantes
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Walter Richardson: Department of Mathematics, University of Texas at San Antonio, San Antonio, TX 78249, USA
Hariharan Krishnaswami: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Rolando Vega: CPS Energy, San Antonio, TX, 78205, USA
Michael Cervantes: Merrick & Company, San Antonio, TX 78258, USA

Sustainability, 2017, vol. 9, issue 4, 1-17

Abstract: With increasing use of photovoltaic (PV) power generation by utilities and their residential customers, the need for accurate intra-hour and day-ahead solar irradiance forecasting has become critical. This paper details the development of a low cost all-sky imaging system and an intra-hour cloud motion prediction methodology that produces minutes-ahead irradiance forecasts. The SkyImager is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in a durable all-weather enclosure. Our software is written in Python 2.7 and utilizes the open source computer vision package OpenCV . The SkyImager can be configured for different operational environments and network designs, from a standalone edge computing model to a fully integrated node in a distributed, cloud-computing based micro-grid. Preliminary results are presented using the imager on site at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA during the fall of 2015 under a variety of cloud conditions.

Keywords: solar forecasting; global horizontal irradiance; single board computer; optical flow; cloud motion vectors; ray tracing; micro-grid (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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