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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.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.publisherElsevier BV
dc.relation.ispartofjournalEnergy Conversion and Management
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchElectronics, sensors and digital hardware
dc.subject.fieldofresearchMechanical engineering
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
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali

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