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dc.contributor.authorAk, Ronay-
dc.contributor.authorFink, Olga-
dc.contributor.authorZio, Enrico-
dc.date.accessioned2018-12-17T09:06:31Z-
dc.date.available2018-12-17T09:06:31Z-
dc.date.issued2016-
dc.identifier.issn2162-237Xde_CH
dc.identifier.issn2162-2388de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13904-
dc.description.abstractThe increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleTwo machine learning approaches for short-term wind speed time-series predictionde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/TNNLS.2015.2418739de_CH
dc.identifier.pmid25910257de_CH
zhaw.funding.euNode_CH
zhaw.issue8de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1747de_CH
zhaw.pages.start1734de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume27de_CH
zhaw.publication.reviewNot specifiedde_CH
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Ak, R., Fink, O., & Zio, E. (2016). Two machine learning approaches for short-term wind speed time-series prediction. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1734–1747. https://doi.org/10.1109/TNNLS.2015.2418739
Ak, R., Fink, O. and Zio, E. (2016) ‘Two machine learning approaches for short-term wind speed time-series prediction’, IEEE Transactions on Neural Networks and Learning Systems, 27(8), pp. 1734–1747. Available at: https://doi.org/10.1109/TNNLS.2015.2418739.
R. Ak, O. Fink, and E. Zio, “Two machine learning approaches for short-term wind speed time-series prediction,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1734–1747, 2016, doi: 10.1109/TNNLS.2015.2418739.
AK, Ronay, Olga FINK und Enrico ZIO, 2016. Two machine learning approaches for short-term wind speed time-series prediction. IEEE Transactions on Neural Networks and Learning Systems. 2016. Bd. 27, Nr. 8, S. 1734–1747. DOI 10.1109/TNNLS.2015.2418739
Ak, Ronay, Olga Fink, and Enrico Zio. 2016. “Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.” IEEE Transactions on Neural Networks and Learning Systems 27 (8): 1734–47. https://doi.org/10.1109/TNNLS.2015.2418739.
Ak, Ronay, et al. “Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, 2016, pp. 1734–47, https://doi.org/10.1109/TNNLS.2015.2418739.


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