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The Use of Uncertainty Quantification for the Empirical Modeling of Wind Turbine Icing
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
Swedish Meteorol & Hydrol Inst, Norrkoping, Sweden.
Swedish Meteorol & Hydrol Inst, Norrkoping, Sweden.
Kapernicus AB, Hallingsjo, Sweden.
2019 (English)In: Journal of Applied Meteorology and Climatology, ISSN 1558-8424, E-ISSN 1558-8432, Vol. 58, no 9, p. 2019-2032Article in journal (Refereed) Published
Abstract [en]

A novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the icing model were identified from the literature and personal communications. These parameters are the median volume diameter of the hydrometeors, the sticking efficiency for snow and graupel, the Nusselt number, the shedding factor, and the wind erosion factor. The sensitivity of these parameters on icing-related wind power production losses is examined. An icing model ensemble representing the estimated parameter uncertainties is designed using so-called deterministic sampling and is run for two periods over a total of 29 weeks. Deterministic sampling allows an exact representation of the uncertainty and, in future applications, further calibration of these parameters. Also, the number of required ensemble members is reduced drastically relative to the commonly used random-sampling method, thus enabling faster delivery and a more flexible system. The results from random and deterministic sampling are compared and agree very well, confirming the usefulness of deterministic sampling. The ensemble mean of the nine-member icing model ensemble generated with deterministic sampling is shown to improve the forecast skill relative to one single forecast for the winter periods. In addition, the ensemble spread provides valuable information as compared with a single forecast in terms of forecasting uncertainty. However, addressing uncertainties in the icing model alone underestimates the forecast uncertainty, thus stressing the need for a fully probabilistic approach in the modeling chain for wind power forecasts in a cold climate.

Place, publisher, year, edition, pages
AMER METEOROLOGICAL SOC , 2019. Vol. 58, no 9, p. 2019-2032
Keywords [en]
Ensembles, Forecasting, Probability forecasts, models, distribution, Short-range prediction, Icing, Renewable energy
National Category
Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:uu:diva-394206DOI: 10.1175/JAMC-D-18-0160.1ISI: 000484447400001OAI: oai:DiVA.org:uu-394206DiVA, id: diva2:1357892
Funder
Swedish Energy Agency, 37279-1Available from: 2019-10-04 Created: 2019-10-04 Last updated: 2019-10-04Bibliographically approved

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