TY - GEN
T1 - Feature extraction of numerical weather prediction results toward reliable wind power prediction
AU - Higashiyama, Kazutoshi
AU - Fujimoto, Yu
AU - Hayashi, Yasuhiro
N1 - Funding Information:
ACKNOWLEDGMENT We thank the Central Research Institute of Electric Power Industry (CRIEPI) for providing NWP datasets. This research was supported by the New Energy and Industrial Technology Development Organization (NEDO).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - deep learning
KW - feature extraction
KW - numerical weather prediction
KW - wind power prediction
UR - http://www.scopus.com/inward/record.url?scp=85046288429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046288429&partnerID=8YFLogxK
U2 - 10.1109/ISGTEurope.2017.8260216
DO - 10.1109/ISGTEurope.2017.8260216
M3 - Conference contribution
AN - SCOPUS:85046288429
T3 - 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
SP - 1
EP - 6
BT - 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017
Y2 - 26 September 2017 through 29 September 2017
ER -