Abstract
Wind power is one of the most attractive forms of electricity from the viewpoints of cost efficiency and environmental protection. However, the instability of wind power has a serious impact on a grid system. Reliable wind power forecasting will help to utilize storage systems and backup generators effectively for mitigating the instability. This paper proposes a feature extraction procedure for numerical weather prediction (NWP) data based on the three-dimensional convolutional neural networks (3DCNNs). An advantage of 3D-CNNs is to automatically extract the spatio-temporal features from NWP data focusing on the targeted wind farm. Feature extraction based on 3D-CNNs was applied to real-world datasets; the results show significant performance in comparison to several benchmark approaches, and also show that the proposed extraction scheme based on 3DCNNs achieves to derive intrinsic features for prediction of wind power generation from NWP data.
| Original language | English |
|---|---|
| Pages (from-to) | 350-358 |
| Number of pages | 9 |
| Journal | Energy Procedia |
| Volume | 155 |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
| Event | 12th International Renewable Energy Storage Conference, IRES 2018 - Dusseldorf, Germany Duration: 2018 Mar 13 → 2018 Mar 15 |
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Keywords
- Convolutional neural networks
- Deep learning
- Feature extraction
- Numerical weather prediction
- Wind power forecasting
ASJC Scopus subject areas
- Energy(all)
Cite this
Feature extraction of NWP data for wind power forecasting using 3D-convolutional neural networks. / Higashiyama, Kazutoshi; Fujimoto, Yu; Hayashi, Yasuhiro.
In: Energy Procedia, Vol. 155, 01.01.2018, p. 350-358.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Feature extraction of NWP data for wind power forecasting using 3D-convolutional neural networks
AU - Higashiyama, Kazutoshi
AU - Fujimoto, Yu
AU - Hayashi, Yasuhiro
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Wind power is one of the most attractive forms of electricity from the viewpoints of cost efficiency and environmental protection. However, the instability of wind power has a serious impact on a grid system. Reliable wind power forecasting will help to utilize storage systems and backup generators effectively for mitigating the instability. This paper proposes a feature extraction procedure for numerical weather prediction (NWP) data based on the three-dimensional convolutional neural networks (3DCNNs). An advantage of 3D-CNNs is to automatically extract the spatio-temporal features from NWP data focusing on the targeted wind farm. Feature extraction based on 3D-CNNs was applied to real-world datasets; the results show significant performance in comparison to several benchmark approaches, and also show that the proposed extraction scheme based on 3DCNNs achieves to derive intrinsic features for prediction of wind power generation from NWP data.
AB - Wind power is one of the most attractive forms of electricity from the viewpoints of cost efficiency and environmental protection. However, the instability of wind power has a serious impact on a grid system. Reliable wind power forecasting will help to utilize storage systems and backup generators effectively for mitigating the instability. This paper proposes a feature extraction procedure for numerical weather prediction (NWP) data based on the three-dimensional convolutional neural networks (3DCNNs). An advantage of 3D-CNNs is to automatically extract the spatio-temporal features from NWP data focusing on the targeted wind farm. Feature extraction based on 3D-CNNs was applied to real-world datasets; the results show significant performance in comparison to several benchmark approaches, and also show that the proposed extraction scheme based on 3DCNNs achieves to derive intrinsic features for prediction of wind power generation from NWP data.
KW - Convolutional neural networks
KW - Deep learning
KW - Feature extraction
KW - Numerical weather prediction
KW - Wind power forecasting
UR - http://www.scopus.com/inward/record.url?scp=85058214403&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058214403&partnerID=8YFLogxK
U2 - 10.1016/j.egypro.2018.11.043
DO - 10.1016/j.egypro.2018.11.043
M3 - Conference article
AN - SCOPUS:85058214403
VL - 155
SP - 350
EP - 358
JO - Energy Procedia
JF - Energy Procedia
SN - 1876-6102
ER -