TY  - JOUR
AU  - Yang, Dazhi
AU  - Alessandrini, Stefano
AU  - Antonanzas, Javier
AU  - Antonanzas-Torres, Fernando
AU  - Badescu, Viorel
AU  - Beyer, Hans Georg
AU  - Blaga, Robert
AU  - Boland, John
AU  - Bright, Jamie M.
AU  - Coimbra, Carlos F. M.
AU  - David, Mathieu
AU  - Frimane, Âzeddine
AU  - Gueymard, Christian A.
AU  - Hong, Tao
AU  - Kay, Merlinde J.
AU  - Killinger, Sven
AU  - Kleissl, Jan
AU  - Lauret, Philippe
AU  - Lorenz, Elke
AU  - van der Meer, Dennis
AU  - Paulescu, Marius
AU  - Perez, Richard
AU  - Perpiñán-Lamigueiro, Oscar
AU  - Peters, Ian Marius
AU  - Reikard, Gordon
AU  - Renné, David
AU  - Saint-Drenan, Yves-Marie
AU  - Shuai, Yong
AU  - Urraca, Ruben
AU  - Verbois, Hadrien
AU  - Vignola, Frank
AU  - Voyant, Cyril
AU  - Zhang, Jie
TI  - Verification of deterministic solar forecasts
JO  - Solar energy
VL  - 210
SN  - 0038-092X
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2021-00713
SP  - 20 - 37
PY  - 2020
AB  - 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 observa tions, 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000579879500003
DO  - DOI:10.1016/j.solener.2020.04.019
UR  - https://juser.fz-juelich.de/record/890124
ER  -