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Ensemble learning for forecasting main meteorological parameters
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering Technological, Educational Institute of Epirus, Arta, Greece.
Computer Technology Institute & Press "Diophantus", Patras, Greece.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-9701-4203
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering Technological Educational Institute of Epirus, Arta, Greece.
2018 (English)In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Piscataway, N.J.: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 3711-3714Conference paper, Published paper (Refereed)
Abstract [en]

The significant role of predicting weather conditions in daily life, the new era of innovative machine learning approaches along with the availability of high volumes of data and high computer performance capabilities, creates increasing perspectives for novel improved short-range forecasting of main meteorological parameters. Among the various algorithms for forecasting parameters, ensemble learning approaches are able to generate simple models which provide accurate predictions for regression problems. The advantage of ensembles with respect to single models is that they perform remarkably well for a variety of problems. The main aim of this ongoing research is to provide some preliminary assessment of the applicability of ensemble learning for wind speed forecasting. In this work, forecasting results of a single and two ensemble models are presented and compared.

Place, publisher, year, edition, pages
Piscataway, N.J.: Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 3711-3714
Series
IEEE International Conference on Systems Man and Cybernetics Conference Proceedings, ISSN 1062-922X
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-67943DOI: 10.1109/SMC.2017.8123210ISI: 000427598703133Scopus ID: 2-s2.0-85044348772OAI: oai:DiVA.org:ltu-67943DiVA, id: diva2:1190682
Conference
IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff Center, Banff, Canada, 5-8 october, 2017
Note

ISBN för värdpublikation: 978-1-5386-1646-8; 978-1-5386-1645-1

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2021-05-27Bibliographically approved

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Publisher's full textScopushttp://www.smc2017.org/

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CiteExportLink to record
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