Verification of deterministic solar forecasts

Dazhi Yang, Stefano Alessandrini, Javier Antonanzas, Fernando Antonanzas-Torres, Viorel Badescu, Hans Georg Beyer, Robert Blaga, John Boland, Jamie M. Bright, Carlos F.M. Coimbra, Mathieu David, Âzeddine Frimane, Christian A. Gueymard, Tao Hong, Merlinde J. Kay, Sven Killinger, Jan Kleissl, Philippe Lauret, Elke Lorenz, Dennis van der MeerMarius Paulescu, Richard Perez, Oscar Perpiñán-Lamigueiro, Ian Marius Peters, Gordon Reikard, David Renné, Yves Marie Saint-Drenan, Yong Shuai, Ruben Urraca, Hadrien Verbois, Frank Vignola, Cyril Voyant, Jie Zhang

Research output: Contribution to journalArticlepeer-review

129 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)20-37
Number of pages18
JournalSolar Energy
Publication statusPublished - 1 Nov 2020


  • Combination of climatology and persistence
  • Distribution-oriented forecast verification
  • Measure-oriented forecast verification
  • Skill score
  • Solar forecasting


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