Verification of deterministic solar forecasts

•This review aims at standardizing the forecast verification approaches used in deterministic solar forecasting.•The distribution-oriented forecast verification framework is introduced.•RMSE skill score is recommended to be universally reported in solar forecasting studies.•A series of practical iss...

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Bibliographic Details
Published in:Solar energy Vol. 210; pp. 20 - 37
Main Authors: Yang, Dazhi, Alessandrini, Stefano, Antonanzas, Javier, Antonanzas-Torres, Fernando, Badescu, Viorel, Beyer, Hans Georg, Blaga, Robert, Boland, John, Bright, Jamie M., Coimbra, Carlos F.M., David, Mathieu, Frimane, Âzeddine, Gueymard, Christian A., Hong, Tao, Kay, Merlinde J., Killinger, Sven, Kleissl, Jan, Lauret, Philippe, Lorenz, Elke, van der Meer, Dennis, Paulescu, Marius, Perez, Richard, Perpiñán-Lamigueiro, Oscar, Peters, Ian Marius, Reikard, Gordon, Renné, David, Saint-Drenan, Yves-Marie, Shuai, Yong, Urraca, Ruben, Verbois, Hadrien, Vignola, Frank, Voyant, Cyril, Zhang, Jie
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.11.2020
Pergamon Press Inc
Elsevier
Subjects:
Accuracy
Climatology
Combination of climatology and persistence
Distribution-oriented forecast verification
Engineering Science with specialization in Civil Engineering and Built Environment
Engineering Sciences
Environmental Sciences
Forecasting
Graphical methods
Mathematical models
Measure-oriented forecast verification
Meteorology
Reliability analysis
Reliability aspects
Root-mean-square errors
Skill score
Solar energy
Solar forecasting
Teknisk fysik med inriktning mot byggteknik och byggd miljö
Verification
Combination of climatology and persistence
Distribution-oriented forecast verification
Solar forecasting
Skill score
Measure-oriented forecast verification
ISSN:0038-092X, 1471-1257, 1471-1257
Online Access:Get full text
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Summary:•This review aims at standardizing the forecast verification approaches used in deterministic solar forecasting.•The distribution-oriented forecast verification framework is introduced.•RMSE skill score is recommended to be universally reported in solar forecasting studies.•A series of practical issues during verification are reviewed. 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.
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ISSN:0038-092X
1471-1257
1471-1257
DOI:10.1016/j.solener.2020.04.019