Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting

Detalhes bibliográficos
Autor(a) principal: Jianhui Wang
Data de Publicação: 2012
Outros Autores: Vladimiro Miranda, Audun Botterud, Zhi Zhou, Ricardo Jorge Bessa
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/2162
Resumo: 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.
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spelling Time-Adaptive Quantile-Copula for Wind Power Probabilistic ForecastingThis 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.2017-11-16T13:17:38Z2012-01-01T00:00:00Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/2162engJianhui WangVladimiro MirandaAudun BotterudZhi ZhouRicardo Jorge Bessainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:09Zoai:repositorio.inesctec.pt:123456789/2162Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:45.172695Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
title Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
spellingShingle Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
Jianhui Wang
title_short Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
title_full Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
title_fullStr Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
title_full_unstemmed Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
title_sort Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting
author Jianhui Wang
author_facet Jianhui Wang
Vladimiro Miranda
Audun Botterud
Zhi Zhou
Ricardo Jorge Bessa
author_role author
author2 Vladimiro Miranda
Audun Botterud
Zhi Zhou
Ricardo Jorge Bessa
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Jianhui Wang
Vladimiro Miranda
Audun Botterud
Zhi Zhou
Ricardo Jorge Bessa
description 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.
publishDate 2012
dc.date.none.fl_str_mv 2012-01-01T00:00:00Z
2012
2017-11-16T13:17:38Z
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