Wind speed prediction for small sample dataset using hybrid first-order accumulated generating operation-based double exponential smoothing model

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dc.citation.issue3
dc.citation.volume10
dc.contributor.authorYousuf MU
dc.contributor.authorAl-Bahadly I
dc.contributor.authorAvci E
dc.date.accessioned2024-03-26T02:16:12Z
dc.date.accessioned2024-07-25T06:48:52Z
dc.date.available2022-03-09
dc.date.available2024-03-26T02:16:12Z
dc.date.available2024-07-25T06:48:52Z
dc.date.issued2022-03-09
dc.description.abstractWind power generation has recently emerged in many countries. Therefore, the availability of long-term historical wind speed data at various potential wind farm sites is limited. In these situations, such forecasting models are needed that comprehensively address the uncertainty of raw data based on small sample size. In this study, a hybrid first-order accumulated generating operation-based double exponential smoothing (AGO-HDES) model is proposed for very short-term wind speed forecasts. Firstly, the problems of traditional Holt's double exponential smoothing model are highlighted considering the wind speed data of Palmerston North, New Zealand. Next, three improvements are suggested for the traditional model with a rolling window of six data points. A mixed initialization method is introduced to improve the model performance. Finally, the superiority of the novel model is discussed by comparing the accuracy of the AGO-HDES model with other forecasting models. Results show that the AGO-HDES model increased the performance of the traditional model by 10%. Also, the modified model performed 7% better than other considered models with three times faster computational time.
dc.description.confidentialfalse
dc.edition.editionMarch 2022
dc.format.pagination726-739
dc.identifier.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000741001800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationYousuf MU, Al-Bahadly I, Avci E. (2022). Wind speed prediction for small sample dataset using hybrid first-order accumulated generating operation-based double exponential smoothing model. Energy Science and Engineering. 10. 3. (pp. 726-739).
dc.identifier.doi10.1002/ese3.1047
dc.identifier.eissn2050-0505
dc.identifier.elements-typejournal-article
dc.identifier.issn2050-0505
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70941
dc.languageEnglish
dc.publisherJohn Wiley & Sons, Inc
dc.publisher.urihttps://onlinelibrary.wiley.com/doi/10.1002/ese3.1047
dc.relation.isPartOfEnergy Science and Engineering
dc.rights(c) 2022 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectexponential
dc.subjectforecasting
dc.subjectHolt
dc.subjecthybrid
dc.subjectsample size
dc.subjectstatistical
dc.subjectwind speed
dc.titleWind speed prediction for small sample dataset using hybrid first-order accumulated generating operation-based double exponential smoothing model
dc.typeJournal article
pubs.elements-id450691
pubs.organisational-groupOther
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