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research-article

Direct Power Output Forecasts from Remote Sensing Image Processing

[+] Author and Article Information
David Larson

Student Member of ASME, Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA 92093-0411
dplarson@ucsd.edu

Carlos F. M. Coimbra

Professor, Member of ASME, Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA 92093-0411
ccoimbra@ucsd.edu

1Corresponding author.

ASME doi:10.1115/1.4038983 History: Received June 05, 2017; Revised December 14, 2017

Abstract

A direct methodology for intra-day forecasts (1-6 hours ahead) of power output (PO) from photovoltaic (PV) solar plants is proposed. The forecasting methodology uses publicly available images from geosynchronous satellites to predict PO directly without resorting to intermediate irradiance (resource) forecasting. Forecasts are evaluated using four years (January 2012-December 2015) of hourly PO data from 2 non-tracking, 1 MWp PV plants in California. For both sites, the proposed methodology achieves forecasting skills ranging from 24--69% relative to reference persistence model results, with root mean square error (RMSE) values ranging from 90-136 kW across the studied horizons. Additionally, we consider the performance of the proposed methodology when applied to imagery from the next generation of geosynchronous satellites, e.g., Himawari-8 and GOES-R.

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