Feature extraction of numerical weather prediction results toward reliable wind power prediction

Kazutoshi Higashiyama, Yu Fujimoto, Yasuhiro Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Wind power prediction is necessary for stable operation of a power grid under the introduction of significant wind power generation. Wind power prediction approaches based on the results of numerical weather prediction (NWP) have been developed successfully in recent years. However, the high dimensionality of NWP results can be a major obstacle when training models. This paper proposes a feature extraction scheme based on convolutional neural networks to compress high-dimensional NWP results by deriving critically important low-dimensional information for wind power prediction. The experimental results show that the proposed feature extractor can contribute to the improvement of wind power prediction accuracy.

Original languageEnglish
Title of host publication2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538619537
DOIs
Publication statusPublished - 2017 Jul 1
Event2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Torino, Italy
Duration: 2017 Sep 262017 Sep 29

Publication series

Name2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017
CountryItaly
CityTorino
Period17/9/2617/9/29

Keywords

  • Convolutional neural network
  • deep learning
  • feature extraction
  • numerical weather prediction
  • wind power prediction

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Feature extraction of numerical weather prediction results toward reliable wind power prediction'. Together they form a unique fingerprint.

  • Cite this

    Higashiyama, K., Fujimoto, Y., & Hayashi, Y. (2017). Feature extraction of numerical weather prediction results toward reliable wind power prediction. In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings (pp. 1-6). (2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGTEurope.2017.8260216