Open AccessNon‐parametric probabilistic forecasts of wind power: required properties and evaluation
Author(s) -
Pinson Pierre,
Nielsen Henrik Aa.,
Møller Jan K.,
Madsen Henrik,
Kariniotakis George N.
Publication year - 2007
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.230
Subject(s) - probabilistic logic , probabilistic forecasting , quantile , parametric statistics , wind power , prediction interval , wind power forecasting , econometrics , point estimation , computer science , power (physics) , mathematics , electric power system , statistics , engineering , machine learning , physics , quantum mechanics , electrical engineering
Predictions of wind power production for horizons up to 48–72 h ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the conditional expectation of the wind generation for each look‐ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from non‐parametric methods, and then take the form of a single or a set of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind power. These distributions are defined by a number of quantile forecasts with nominal proportions spanning the unit interval. The relevance and interest of the introduced evaluation framework are discussed. Copyright © 2007 John Wiley & Sons, Ltd.