Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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-s2.0-8504434877ISBN: 978-1-5386-1646-8 (print)ISBN: 978-1-5386-1645-1 (electronic)OAI: 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
Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-04-19Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

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

Search in DiVA

By author/editor
Georgoulas, Georgios
By organisation
Signals and Systems
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf