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dc.contributor.authorNeshat, M
dc.contributor.authorNezhad, MM
dc.contributor.authorAbbasnejad, E
dc.contributor.authorMirjalili, S
dc.contributor.authorTjernberg, LB
dc.contributor.authorAstiaso Garcia, D
dc.contributor.authorAlexander, B
dc.contributor.authorWagner, M
dc.date.accessioned2021-05-24T02:44:22Z
dc.date.available2021-05-24T02:44:22Z
dc.date.issued2021
dc.identifier.issn0196-8904
dc.identifier.doi10.1016/j.enconman.2021.114002
dc.identifier.urihttp://hdl.handle.net/10072/404600
dc.description.abstractDue to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom114002
dc.relation.ispartofjournalEnergy Conversion and Management
dc.relation.ispartofvolume236
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchElectronics, sensors and digital hardware
dc.subject.fieldofresearchMechanical engineering
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode4009
dc.subject.fieldofresearchcode4017
dc.titleA deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationNeshat, M; Nezhad, MM; Abbasnejad, E; Mirjalili, S; Tjernberg, LB; Astiaso Garcia, D; Alexander, B; Wagner, M, A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm, Energy Conversion and Management, 2021, 236, pp. 114002
dc.date.updated2021-05-23T22:21:22Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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