Verification of deterministic solar forecasts

Yang D, Alessandrini S, Antonanzas J, Antonanzas-Torres F, Badescu V, Beyer HG, Blaga R, Boland J, Bright JM, Coimbra CFM, David M, Frimane A, Gueymard CA, Hong T, Kay MJ, Killinger S, Kleissl J, Lauret P, Lorenz E, Van Der Meer D, Paulescu M, Perez R, Perpinan-Lamigueiro O, Peters IM, Reikard G, Renne D, Saint-Drenan YM, Shuai Y, Urraca R, Verbois H, Vignola F, Voyant C, Zhang J (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 210

Pages Range: 20-37

DOI: 10.1016/j.solener.2020.04.019

Abstract

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows—with appropriate caveats—comparison of forecasts made using different models, across different locations and time periods.

Involved external institutions

University of California, San Diego United States (USA) (US) Fraunhofer-Institut für Solare Energiesysteme (ISE) / Fraunhofer Institute for Solar Energy Systems Germany (DE) Agency for Science, Technology and Research (A*STAR) Singapore (SG) National Center for Atmospheric Research (NCAR) United States (USA) (US) Universidad de La Rioja Spain (ES) Politechnic University of Bucharest / Universitatea Politehnica din București Romania (RO) University of the Faroe Islands Faroe Islands (FO) West University Timisoara (WUT) Romania (RO) University of South Australia Australia (AU) National University of Singapore (NUS) Singapore (SG) Université de La Réunion France (FR) Université Ibn Tofaïl (UIT) / جامعة ابن طفيل Morocco (MA) Solar Consulting Services United States (USA) (US) University of North Carolina at Charlotte (UNCC) United States (USA) (US) University of New South Wales (UNSW) Australia (AU) Uppsala University Sweden (SE) State University of New York at Albany (UNY Albany / UAlbany) United States (USA) (US) Universidad Politécnica de Madrid (UPM) Spain (ES) Massachusetts Institute of Technology (MIT) United States (USA) (US) US Cellular United States (USA) (US) Dave Renné Renewables United States (USA) (US) PSL Research University / Université de recherche Paris Sciences et Lettres France (FR) Harbin Institute of Technology (HIT) / 哈工大 China (CN) University of Oregon (UO) United States (USA) (US) University of Texas at Dallas (UTD / UT Dallas) United States (USA) (US)

How to cite

APA:

Yang, D., Alessandrini, S., Antonanzas, J., Antonanzas-Torres, F., Badescu, V., Beyer, H.G.,... Zhang, J. (2020). Verification of deterministic solar forecasts. Solar Energy, 210, 20-37. https://doi.org/10.1016/j.solener.2020.04.019

MLA:

Yang, Dazhi, et al. "Verification of deterministic solar forecasts." Solar Energy 210 (2020): 20-37.

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