Analyzing load profiles of electricity consumption by a time series data mining framework

I. Chin Wu, Tzu Li Chen, Yen Ming Chen, Tzu Chi Liu, Yi An Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given the problems of gradual oil depletion and global warming, energy consumption has become a critical factor for energy-intensive sectors, especially the semiconductor, manufacturing, iron and steel, and aluminum industries. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical challenges. In response, we analyzed electricity consumption from the perspective of load profiling, which charts variation in electrical load during a specified period in order to track energy consumption. As a result, we proposed a time series data mining and analytic framework for electricity consumption analysis and pattern extraction by streaming data mining and machine learning techniques. We identified key factors to predict the state of the annealing furnace and detect abnormal patterns of the load profile of their electricity consumption. Our experimental results show that the dimension reduction method known as piecewise aggregate approximation can help to detect the state of the annealing furnace.

Original languageEnglish
Title of host publicationHCI in Business, Government and Organizations
Subtitle of host publicationSupporting Business - 4th International Conference, HCIBGO 2017 Held as Part of HCI International 2017, Proceedings
EditorsChuan-Hoo Tan, Fiona Fui-Hoon Nah
PublisherSpringer Verlag
Pages443-454
Number of pages12
ISBN (Print)9783319584836
DOIs
Publication statusPublished - 2017 Jan 1
Externally publishedYes
Event14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2017 - Espoo, Finland
Duration: 2017 Jul 32017 Jul 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10294 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2017
CountryFinland
CityEspoo
Period17/7/317/7/6

    Fingerprint

Keywords

  • Energy consumption analysis
  • Load profiling
  • Piecewise aggregate approximation
  • Time-series data mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wu, I. C., Chen, T. L., Chen, Y. M., Liu, T. C., & Chen, Y. A. (2017). Analyzing load profiles of electricity consumption by a time series data mining framework. In C-H. Tan, & F. F-H. Nah (Eds.), HCI in Business, Government and Organizations: Supporting Business - 4th International Conference, HCIBGO 2017 Held as Part of HCI International 2017, Proceedings (pp. 443-454). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10294 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-58484-3_35