 Nonparametric wind power forecasting under fixed and random censoringChristian Dahl, Georgios Effraimidis and Mikkel H. Pedersen Energy Economics, 2019, vol. 84, issue C Abstract: We consider nonparametric forecasting of wind power for individual wind turbines, allowing for random right censoring as well as two-sided fixed censoring. We propose a very fast estimation algorithm and show that this estimator of the unknown regression function is uniformly consistent. We argue that the key statistical features of the proposed nonparametric regression framework, such as nonlinearities and fixed and random censoring, are all needed in order to properly capture the main characteristics of wind power production functions. We show by a simulation study that the asymptotic properties of the estimator also holds in finite samples. We provide an empirical illustration comparing the forecasting accuracy of the proposed nonparametric regression model to some of the existing and popular forecasting devices used for predicting short to medium term wind power production at the individual turbine level. The empirical results are generally very encouraging. Keywords: Kaplan-Meier estimator; Nonparametric methods; Time series (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303093 DOI: 10.1016/j.eneco.2019.104520 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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