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@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},
}