Open AccessEvaluation of wind power forecasts—An up‐to‐date view
Author(s) -
Messner Jakob W.,
Pinson Pierre,
Browell Jethro,
Bjerregård Mathias B.,
Schicker Irene
Publication year - 2020
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2497
Subject(s) - forecast verification , wind power forecasting , computer science , perspective (graphical) , forecast error , wind power , task (project management) , selection (genetic algorithm) , consensus forecast , key (lock) , operations research , data mining , industrial engineering , power (physics) , systems engineering , engineering , machine learning , artificial intelligence , econometrics , electric power system , mathematics , physics , electrical engineering , computer security , quantum mechanics
Wind power forecast evaluation is of key importance for forecast provider selection, forecast quality control, and model development. While forecasts are most often evaluated based on squared or absolute errors, these error measures do not always adequately reflect the loss functions and true expectations of the forecast user, neither do they provide enough information for the desired evaluation task. Over the last decade, research in forecast verification has intensified, and a number of verification frameworks and diagnostic tools have been proposed. However, the corresponding literature is generally very technical and most often dedicated to forecast model developers. This can make forecast users struggle to select the most appropriate verification tools for their application while not fully appraising subtleties related to their application and interpretation. This paper revisits the most common verification tools from a forecast user perspective and discusses their suitability for different application examples as well as evaluation setup design and significance of evaluation results.