ICT-ENSURE Information System on Literature in the Field of ICT for Environmental Sustainability

ICT-ENSURE Information System on Literature in the Field of ICT for Environmental Sustainability ICT-ENSURE Information System on Literature in the Field of ICT for Environmental Sustainability European Commission European Commission CORDIS Seventh Framework Programme KIT - Karlsruhe Institute of Technology Graz University of Technology, Knowledge Management Institute International Society for Environmental Protection
Full text search fulltext search
Hints for fulltext search
  • Use AND or && if the terms shall both be contained
  • Use OR if at least of the terms shall be contained
  • Use "..." (quotes) to group multiple words
  • Use * (asterisk) for wildcards
  • Use NOT or - (minus) if the term shall not be contained
Note: These commands are case-sensitive
Search by criteria

Wind Power Prediction with Cross-Correlation Weighted Nearest Neighbors

Authors
Treiber, Nils André (University of Oldenburg)
Search for Nils André Treiber in Research Programmes Information System
Kramer, Oliver (University of Oldenburg)
Search for Oliver Kramer in Research Programmes Information System
Chapter Renewable Energy
Volume Proceedings of the 28th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management
Conference EnviroInfo 2014 - ICT for Energy Effieciency
Oldenburg, 2014
Year 2014
Abstract of the Article
A precise wind power prediction is important for the integration of wind energy into the power
grid. Besides numerical weather models for short-term predictions, there is a trend towards the
development of statistical data-driven models that can outperform the classical forecast models [1].
In this paper, we improve a statistical prediction model proposed by Kramer and Gieseke [5], by
employing a cross-correlation weighted k-nearest neighbor regression model (x-kNN). We
demonstrate its superior performance by the comparison with the standard u-kNN method. Even if
different pre-processing steps are considered, our regression technique achieves a comparably high
accuracy.
Pages 63 - 68
Cite Download cite reference as: RIS | EndNote | BibTeX
Download Go to fulltext article (external link)
Download article from the web site of:
Server of Fachgruppe Umweltinformatik of the Gesellschaft für Informatik
(this is an external link, please check the Legal Notice on the bottom of the page)

Version 1.2 © 2008-2018 ICT-ENSURE consortium.

If you encounter any errors, please feel encouraged to contact us via e-mail.

ict-ensureiaifzkde