Deep learning based ensemble approach for probabilistic wind power forecasting
Shenzhen University · Shanghai Tunnel Engineering Rail Transit Design & Research Institute
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Abstract
No abstract available for this paper.
Citation impact
735
total citations
- FWCI
- 32.73
- Percentile
- 100%
- References
- 46
Citations per year
Authors
6Topics & keywords
Topics
Keywords
- Wind power
- Probabilistic logic
- Wind power forecasting
- Probabilistic forecasting
- Computer science
- Wavelet transform
- Electric power system
- Artificial neural network
UN Sustainable Development Goals
- Affordable and clean energy
No related works found for this paper.
Funding
- NNNational Natural Science Foundation of ChinaAwards: 51477104, 51507103
- NSNatural Science Foundation of Guangdong ProvinceAwards: 2016035, JCYJ20150525092941041, 2016A030313041, 2015030, GJHZ20150313093836007, 2013CB228202, 2015A030310316, JCYJ20160422165525693
- NKNational Key Research and Development Program of China