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

Kazutoshi Higashiyama, Yu Fujimoto, Yasuhiro Hayashi

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
ISBN(電子版)9781538619537
DOI
出版ステータスPublished - 2017 7 1
イベント2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Torino, Italy
継続期間: 2017 9 262017 9 29

出版物シリーズ

名前2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
2018-January

Other

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

ASJC Scopus subject areas

  • 電子工学および電気工学
  • エネルギー工学および電力技術
  • コンピュータ ネットワークおよび通信

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