초록
In this paper, we propose a time-series generation methodology using a generative adversarial network (GAN) for long-term prediction of wind and sea conditions, which are information necessary for operations and maintenance (O&M) planning and optimal plans for offshore wind farms. It is a “Conditional TimeGAN” that is able to control time-series data with monthly conditions while maintaining a time dependency between time-series. For the generated time-series data, the similarity of the statistical distribution by direction was confirmed through wave and wind rose diagram visualization. It was also found that the statistical distribution and feature correlation between the real data and the generated time-series data was similar through PCA, t-SNE, and heat map visualization algorithms. The proposed time-series generation methodology can be applied to monthly or annual marine weather prediction including probabilistic correlations between various features (wind speed, wind direction, wave height, wave direction, wave period and their time-series characteristics). It is expected that it will be able to provide an optimal plan for the maintenance and optimization of offshore wind farms based on more accurate long-term predictions of sea and wind conditions by using the proposed model.
키워드
시계열, 생성적 적대 신경망, 딥러닝, 바람, 파랑, 해상풍력발전단지, 운영 및 유지정비
참고문헌(15)
-
[학술지] 상민규 / 2021 / 부유식 해상풍력발전 유지보수 계획 최적화 모형 개발 / 한국컴퓨터정보학회논문지 26 (12) : 255 ~ 264
-
[학술지] Ro, H. Y. / 1969 / Problems on design and construction used heavy equipments / Journal of Korean Society of Civil Engineers 17 (1) : 54 ~ 69
-
[학술지] 최세호 / 2019 / 파랑을 고려한 항만 건설공사 작업일수 산정에 관한 연구 / 한국연안방재학회지 6 (2) : 71 ~ 82
-
[학술지] 백종대 / 2021 / 울산신항의 파랑을 이용한 항만공사 작업일수 산정방법 연구 / 한국해안·해양공학회논문집 33 (2) : 80 ~ 91
-
[보고서] Ministry of Oceans and Fisheries / 2020 / Report on the design practice for harbor construction project
-
[학술지] 최세호 / 2019 / 해상작업 가능기간 산정을 위한 확률모형 개발 - 울산항 전면 해역을 중심으로 / 한국해안·해양공학회논문집 31 (3) : 115 ~ 128
-
[학술지] Goodfellow, I. / 2014 / Generative adversarial nets / Advances in neural information processing systems 27
-
[기타] Radford, A. / 2015 / Unsupervised representation learning with deep convolutional generative adversarial networks / arXiv preprint arXiv:1511.06434
-
[기타] Berthelot, D. / 2017 / Began: Boundary equilibrium generative adversarial networks / arXiv preprint arXiv:1703.10717
-
[기타] Zhao, J. / 2016 / Energy-based generative adversarial network / arXiv preprint arXiv:1609.03126
-
[학술대회] Arjovsky, M. / 2017 / Wasserstein generative adversarial networks / International conference on machine learning : 214 ~ 223
-
[기타] Mogren, O. / 2016 / C-RNN-GAN: Continuous recurrent neural networks with adversarial training / arXiv preprint arXiv:1611.09904
-
[기타] Esteban, C. / 2017 / Real-valued (medical) time series generation with recurrent conditional gans / arXiv preprint arXiv:1706.02633
-
[학술지] Yoon, J. S. / 2019 / Time-series Generative Adversarial Networks / Advances in Neural Information Processing Systems 32
-
[기타] Mirza, M. / 2014 / Conditional Generative Adversarial Nets / arXiv preprint arXiv:1411.1784