Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
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 |