% IMPORTANT: The following is UTF-8 encoded. This means that in the presence % of non-ASCII characters, it will not work with BibTeX 0.99 or older. % Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or % “biber”. @ARTICLE{Yang:890124, author = {Yang, Dazhi and Alessandrini, Stefano and Antonanzas, Javier and Antonanzas-Torres, Fernando and Badescu, Viorel and Beyer, Hans Georg and Blaga, Robert and Boland, John and Bright, Jamie M. and Coimbra, Carlos F. M. and David, Mathieu and Frimane, Âzeddine and Gueymard, Christian A. and Hong, Tao and Kay, Merlinde J. and Killinger, Sven and Kleissl, Jan and Lauret, Philippe and Lorenz, Elke and van der Meer, Dennis and Paulescu, Marius and Perez, Richard and Perpiñán-Lamigueiro, Oscar and Peters, Ian Marius and Reikard, Gordon and Renné, David and Saint-Drenan, Yves-Marie and Shuai, Yong and Urraca, Ruben and Verbois, Hadrien and Vignola, Frank and Voyant, Cyril and Zhang, Jie}, title = {{V}erification of deterministic solar forecasts}, journal = {Solar energy}, volume = {210}, issn = {0038-092X}, address = {Amsterdam [u.a.]}, publisher = {Elsevier Science}, reportid = {FZJ-2021-00713}, pages = {20 - 37}, year = {2020}, 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 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.}, cin = {IEK-11}, ddc = {530}, cid = {I:(DE-Juel1)IEK-11-20140314}, pnm = {121 - Solar cells of the next generation (POF3-121)}, pid = {G:(DE-HGF)POF3-121}, typ = {PUB:(DE-HGF)16}, UT = {WOS:000579879500003}, doi = {10.1016/j.solener.2020.04.019}, url = {https://juser.fz-juelich.de/record/890124}, }