Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting

dc.contributor.author Jianhui Wang en
dc.contributor.author Vladimiro Miranda en
dc.contributor.author Audun Botterud en
dc.contributor.author Zhi Zhou en
dc.contributor.author Ricardo Jorge Bessa en
dc.date.accessioned 2017-11-16T13:17:38Z
dc.date.available 2017-11-16T13:17:38Z
dc.date.issued 2012 en
dc.description.abstract This paper presents a novel time-adaptive quantile-copula estimator for kernel density forecast and a discussion of how to select the adequate kernels for modeling the different variables of the problem. Results are presented for different case-studies and compared with splines quantile regression (QR). The datasets used are from NREL's Eastern Wind Integration and Transmission Study, and from a real wind farm located in the Midwest region of the United States. . The new probabilistic forecasting model is elegant and simple and yet displays advantages over the traditional QR approach. Especially notable is the quality of the results achieved with the time-adaptive version, namely when evaluated in terms of forecast calibration, which is a characteristic that is advantageous for both system operators and wind power producers. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/2162
dc.language eng en
dc.relation 4882 en
dc.relation 208 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting en
dc.type article en
dc.type Publication en
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