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Bayesian neural networks for one-hour ahead wind power forecasting
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0001-6553-823X
2017 (English)In: 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, Vol. 2017, p. 591-596Conference paper, Published paper (Refereed)
Abstract [en]

The greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian regularization is therefore seen as a possible technique for reducing model overfitting problems that may arise. This work investigates Bayesian Neural Networks for one-hour ahead forecasting of wind power generation. Initial results show that Bayesian Neural Networks display equivalent predictive performance to Neural Networks trained by Maximum Likelihood. Further results show that Bayesian Neural Networks become superior after removing irrelevant features using Automatic Relevance Determination(ARD).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 2017, p. 591-596
Series
International Conference on Renewable Energy Research and Applications
Keywords [en]
Ahead, Automatic relevance determination, Bayesian, Forecasting, Neural networks, One-hour, Wind power
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-224241DOI: 10.1109/DISTRA.2017.8191129ISI: 000426708600096Scopus ID: 2-s2.0-85042722249ISBN: 9781538620953 OAI: oai:DiVA.org:kth-224241DiVA, id: diva2:1190694
Conference
6th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2017, 5 November 2017 through 8 November 2017, San Diego, United States
Note

QC 20180315

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-03-23Bibliographically approved

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Herman, Pawel

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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