Catching Uncertainty of Wind: A Blend of Sieve Bootstrap and Regime Switching Models for Probabilistic Short-Term Forecasting of Wind Speed
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Although clean and sustainable wind energy has long been recognized as one of the most attractive electric power sources, generation of wind power is still much easier than its integration into liberalized electricity markets. One of the key obstacles on the way of wider implementation of wind energy is its highly volatile and intermittent nature. This has boosted an interest in developing a fully probabilistic forecast of wind speed, aiming to assess a variety of related uncertainties. Nonetheless, most of the available methodology for constructing a future predictive density for wind speed are based on parametric distributional assumptions on the observed wind data, and such conditions are often too restrictive and infeasible in practice. In this paper we propose a new nonparametric data-driven approach to probabilistic wind speed forecasting, adaptively combining sieve bootstrap and regime switching models. Our new bootstrapped regime switching (BRS) model delivers highly competitive, sharp and calibrated ensembles of wind speed forecasts, governed by various states of wind direction, and imposes minimal requirements on the observed wind data. The proposed methodology is illustrated by developing probabilistic wind speed forecasts for a site in the Washington State, USA.
KeywordsRenewable energy Resampling Power grid Predictive density Sustainability Nonparametrics
The authors would like to thank the Bonneville Power Administration and the Oregon State University Energy Resources Research Laboratory, particularly, Stel Walker, for the generosity in providing the wind speed and direction data. We would like to thank William Weimin Yoo and Kimihiro Noguchi for the help at the initial stage of this project. Research of Ejaz Ahmed is supported in part by the Grant from the Natural Sciences and Engineering Research Council of Canada, and Yulia R. Gel is supported in part by the National Science Foundation Grant DMS1514808.
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