Adaptive Generalized Logit-Normal Distributions for Wind Power Short-Term Forecasting

Amandine Pierrot, Pierre Pinson

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


There is increasing interest in very short-term and higher-resolution wind power forecasting (from mins to hours ahead), especially offshore. Statistical methods are of utmost relevance, since weather forecasts cannot be informative for those lead times. Those approaches ought to account for the fact wind power generation as a stochastic process is non-stationary, double-bounded (by zero and the nominal power of the turbine) and non-linear. Accommodating those aspects may lead to improving both point and probabilistic forecasts. We propose to focus on generalized logit-normal distributions, which are naturally suitable and flexible for double-bounded and non-linear processes. Relevant parameters are estimated via maximum likelihood inference. Both batch and online versions of the estimation approach are described - the online version permitting to additionally handle non-stationarity through parameter variation. The approach is applied and analysed on the test case of the Anholt offshore wind farm in Denmark, with emphasis placed on 10-min-ahead forecasting.
Original languageEnglish
Title of host publication Proceedings of the 2021 IEEE Madrid Powertech
Publication date2021
Article number9494900
Publication statusPublished - 2021
Event2021 IEEE Madrid PowerTech - Virtual Event - from the Alberto Aguilera Campus of Comillas University, Madrid, Spain
Duration: 28 Jun 20212 Jul 2021
Conference number: 14


Conference2021 IEEE Madrid PowerTech
LocationVirtual Event - from the Alberto Aguilera Campus of Comillas University
Internet address


  • Wind power
  • Probabilistic forecasting
  • Dynamic models
  • Bounded time-series


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