题名

臺灣離岸風場巨量資料平台開發與智慧化管理

并列篇名

DEVELOPING A BIG DATA PLATFORM FOR INTELLIGENT MANAGEMENT OF OFFSHORE WIND FARM IN TAIWAN

作者

廖偉傑(W.C. Liao);王佑鈞(Y.C. Wang);顧晨生(C.S. Ku);吳伯彥(P.Y. Wu);林宣佑(H.Y. Lin);張瑞益(R.I. Chang)

关键词

巨量資料平台 ; 離岸風場 ; 機器學習 ; 資料視覺化 ; Big Data Web Platform ; Offshore Wind Farm ; Machine Learning ; Data Visualization

期刊名称

中國造船暨輪機工程學刊

卷期/出版年月

37卷3期(2018 / 08 / 01)

页次

99 - 106

内容语文

繁體中文

中文摘要

本研究建置一套離岸風場巨量資料平台,提供各種功能模組給使用者,達到離岸風場智慧化管理之目標。架構上包括四個模組,分別為動態儀表板模組、資料蒐集模組、資料視覺化模組、資料分析模組。動態儀表板模組提供一個動態編輯介面,以供使用者客製化設計及展示各項視覺化圖表,可以整合不同使用者所開發的成果,使合作及展示的效益最大化。資料蒐集模組建置了巨量資料的檔案系統與資料庫,並提供支援感測資料蒐集之服務,以利使用者上傳蒐集的風力機資訊。資料視覺化模組提供圖表功能,以供使用者自由選擇,對巨量資料進行視覺化呈現。資料分析模組提供機器學習之相關功能,以供使用者對資料進行特徵選取及各種分析。

英文摘要

In this study, a web-based platform was built for providing the function modules to collect and store the big data of wind turbines in the offshore wind farm. These four function modules are called DD (Dynamic Dashboard), DC (Data Collection), DV (Data Visualization), and DA (Data Analysis). The DD module provides an editing interface for users to dynamically present their designed visualizations. It can also integrate the results from different users to benefit their collaborations. The DC module builds file systems and databases for storing big data. It provides a service to collect big data where users can upload their collected data. The DV module provides different plots and charts functions for users to design the visualizations of data. The DA module provides machine learning functions for users to select features and analyze data. Through these modules, we can make an intelligent management of offshore wind farms.

主题分类 工程學 > 機械工程
工程學 > 交通運輸工程
参考文献
  1. GitLab Documentation, Retrieved from https://docs.gitlab.com/ee/README.html (2018)
  2. Django documentation, Retrieved from https://docs.djangoproject.com/en/2.1/ (2018)
  3. TensorFlow Guide, Retrieved from https://www.tensorflow.org/guide/ (2018)
  4. FAQ: General | Django documentation | Django, Retrieved from https://docs.djangoproject.com/en/2.1/faq/general/ (2018).
  5. HDFS Architecture Guide, Retrieved from https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html (2018).
  6. MATLAB Documentation - MathWorks, Retrieved from https://www.mathworks.com/help/matlab/ (2018).
  7. Welcome to the Highcharts JS Options Reference, Retrieved from https://api.highcharts.com/highcharts/ (2018)
  8. Keras: The Python Deep Learning library, Retrieved from https://keras.io (2018)
  9. Grid system·Bootstrap, Retrieved from https://getboot-strap.com/docs/4.1/layout/grid/ (2018)
  10. Apache HBase–Apache HBase™ Home, Retrieved from https://hbase.apache.org (2018)
  11. Guide-Vue.js, Retrieved from https://vuejs.org/v2/guide/ (2018).
  12. Chang, R.I.,Huang, C.C.,Lai, L.B.,Lee, C.Y.(2018).Query-based machine learning model for data analysis of infrasonic signals in wireless sensor networks.Proc. 2nd Int. Conf. Digit. Signal Process,Tokyo, Japan:
  13. Chen, C.P.,Zhang, C.Y.(2014).Data-intensive applications, challenges, techniques and technologies: A survey on big data.Inform. Sciences,275,314-347.
  14. Christie, T., Django REST framework: Home, Retrieved from http://www.django-rest-framework.org/ (2017)
  15. Cox, M.,Ellsworth, D.(1997).Managing big data for scientific visualization.ACM Siggraph,97,21-38.
  16. Fayyad, U.M.,Piatetsky-Shapiro, G.,Smyth, P.,Uthurusamy, R.(1996).Advances in Knowledge Discovery and Data Mining.Advances in Knowledge Discovery and Data Mining,Menlo Park, CA, USA:
  17. Ferguson, M.(2012).Ferguson, M., “Architecting a big data platform for analytics,” A Whitepaper prepared for IBM (2012)..
  18. Helsen, J.,Jordaens, P. Jan,Desitter, G.(2015).Big data intelligence platform for wind turbines to support RD&I projects.Proc. EWEA 2015,Paris, France:
  19. Hong, X.,Jianhua, W.(2006).Using standard components in automation industry: A study on OPC specification.Comput. Stand. Inter.,28,386-395.
  20. John Walker, S(2014).Big data: A revolution that will transform how we live, work, and think.Int. J. Advert.,33,181-183.
  21. Lee, J.,Bagheri, B.,Kao, H.A.(2015).A cyber-physical systems architecture for industry 4.0-based manufacturing systems.Manuf. Lett.,3,18-23.
  22. Lee, J.,Kao, H.A.,Yang, S.(2014).Service innovation and smart analytics for industry 4.0 and big data environment.Procedia CIRP,16,3-8.
  23. Lin, J.-Y.,Lee, C.-Y.,Chang, R.-I.(2018).Improve quality and efficiency of textile process using data-driven machine learning in Industry 4.0,.Int. Symp. Theory Pract. IT, Eng. Appl. Sciences (TPIEA),Tokyo, Japan:
  24. Matthew, N.,Stones, R.(2005).Beginning databases with PostgreSQL.Berkley, CA, USA:Apress.
  25. Najafabadi, M.M.,Villanustre, F.,Khoshgoftaar, T.M.,Seliya, N.,Wald, R.,Muharemagic, E.(2015).Deep learning applications and challenges in big data analytics.J. Big Data,2,1-21.
  26. 余承叡,盧冠宇,吳維文,丁士翔(2016)。邁向工業4.0─製造業的大數據分析應用實例。電工通訊季刊,2016(2),68-77。
被引用次数
  1. 詹婉渝,張瑞益,邱于菱(2019)。使用活動流與圖形資料庫之工安訓練系統規劃設計。資訊與管理科學,12(2),4-18。