A CDF-based symbolic time-series data mining approach for electricity consumption analysis

I. Chin Wu*, Yi An Chen, Zan Xian Wang

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

摘要

Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. This study collects the energy consumption data of annealing processes from an annealing furnace of a co-operating steel forging plant. We propose a CDF-based symbolic time-series data mining and analytic framework for electricity consumption analysis and prediction of machine operating states by machine-learning techniques. We computed the breakpoint value relying on a density-based notion – namely, the cumulative distribution function (CDF) – to improve the original breakpoint table in the SAX algorithm for symbolizing the time-series data. The main contribution of this work is that the modified SAX algorithm can achieve better prediction the operating state of the machine in comparison to the original SAX algorithm.

原文英語
主出版物標題HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings
編輯Constantine Stephanidis
發行者Springer Verlag
頁面515-521
頁數7
ISBN(列印)9783319922843
DOIs
出版狀態已發佈 - 2018 一月 1
事件20th International Conference on HCI, HCI International 2018 - Las Vegas, 美国
持續時間: 2018 七月 152018 七月 20

出版系列

名字Communications in Computer and Information Science
852
ISSN(列印)1865-0929

會議

會議20th International Conference on HCI, HCI International 2018
國家/地區美国
城市Las Vegas
期間2018/07/152018/07/20

ASJC Scopus subject areas

  • 電腦科學(全部)
  • 數學(全部)

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