Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3707
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dc.contributor.authorZhen, Zhao-
dc.contributor.authorQiu, Gang-
dc.contributor.authorShengwei, Mei-
dc.contributor.authorWang, Fei-
dc.contributor.authorZhang, Xuemin-
dc.contributor.authorYin, Rui-
dc.contributor.authorLi, Yu-
dc.contributor.authorOsório, Gerardo J.-
dc.contributor.authorShafie-khah, Miadreza-
dc.contributor.authorCatalão, João P. S.-
dc.date.accessioned2021-10-15T11:42:34Z-
dc.date.available2021-10-15T11:42:34Z-
dc.date.issued2022-02-
dc.identifier.citationZhen, Z., Qiu, G., Mei, S., Wang, F., Zhang, X., Yin, R., Li, Y, Osório, G. J., Shafie-khah, M., & Catalão, J. P. S. (2022). An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive nodeling. International Journal of Electrical Power & Energy Systems, , 135(107502). Doi: 10.1016/j.ijepes.2021.107502. Disponível no Repositório UPT, http://hdl.handle.net/11328/3707pt_PT
dc.identifier.issn0142-0615-
dc.identifier.urihttp://hdl.handle.net/11328/3707-
dc.description.abstractThe forecast of wind speed is a prerequisite for wind power prediction, which is one of the most effective means of promoting wind power absorption. However, when modeling for wind speed sequences with different fluctuations, most existing researchers ignore the influence of the time scale of wind speed fluctuation period, let alone the low compatibility between training and testing samples that severely limit the training performance of the forecasting model. To improve the accuracy of wind speed and wind power forecasting, an ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling is proposed in this paper. First, a series of wind processes are divided from the historical wind speed sequence according to the natural variation characteristics of wind speed. Second, we divide all the wind processes into two patterns based on their time scale, and an SVC model with input features extracted from meteorological data is built to identify the time scale of the current wind process. Third, for a specifically identified wind process, the complex network algorithm is applied in data screening to select high compatible training samples to train the forecast model dynamically for current input. The simulation indicates that the proposed approach presents higher accuracy than benchmark models using the same forecasting algorithms but without considering the time scale and data screening.pt_PT
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.rightsrestrictedAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWind speed forecastpt_PT
dc.subjectWind processpt_PT
dc.subjectTime scale distribution functionpt_PT
dc.subjectPattern recognitionpt_PT
dc.subjectComplex networkpt_PT
dc.titleAn ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modelingpt_PT
dc.typearticlept_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0142061521007419?via%3Dihubpt_PT
dc.peerreviewedyespt_PT
degois.publication.volume135pt_PT
degois.publication.issue107502pt_PT
degois.publication.titleInternational Journal of Electrical Power & Energy Systemspt_PT
dc.identifier.doi10.1016/j.ijepes.2021.107502pt_PT
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals

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