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

I. Chin Wu; Yi An Chen; Zan Xian Wang

doi:10.1007/978-3-319-92285-0_71

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

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings
Editors	Constantine Stephanidis
Publisher	Springer Verlag
Pages	515-521
Number of pages	7
ISBN (Print)	9783319922843
DOIs	https://doi.org/10.1007/978-3-319-92285-0_71
Publication status	Published - 2018
Event	20th International Conference on HCI, HCI International 2018 - Las Vegas, United States Duration: 2018 Jul 15 → 2018 Jul 20

Publication series

Name	Communications in Computer and Information Science
Volume	852
ISSN (Print)	1865-0929

Conference

Conference	20th International Conference on HCI, HCI International 2018
Country/Territory	United States
City	Las Vegas
Period	2018/07/15 → 2018/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cumulative distribution function
Electricity consumption analysis
Symbolic aggregate approximation
Time-series data mining

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-319-92285-0_71

Cite this

Wu, I. C., Chen, Y. A., & Wang, Z. X. (2018). A CDF-based symbolic time-series data mining approach for electricity consumption analysis. In C. Stephanidis (Ed.), HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings (pp. 515-521). (Communications in Computer and Information Science; Vol. 852). Springer Verlag. https://doi.org/10.1007/978-3-319-92285-0_71

A CDF-based symbolic time-series data mining approach for electricity consumption analysis. / Wu, I. Chin; Chen, Yi An; Wang, Zan Xian.
HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings. ed. / Constantine Stephanidis. Springer Verlag, 2018. p. 515-521 (Communications in Computer and Information Science; Vol. 852).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, IC, Chen, YA & Wang, ZX 2018, A CDF-based symbolic time-series data mining approach for electricity consumption analysis. in C Stephanidis (ed.), HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings. Communications in Computer and Information Science, vol. 852, Springer Verlag, pp. 515-521, 20th International Conference on HCI, HCI International 2018, Las Vegas, United States, 2018/07/15. https://doi.org/10.1007/978-3-319-92285-0_71

Wu IC, Chen YA, Wang ZX. A CDF-based symbolic time-series data mining approach for electricity consumption analysis. In Stephanidis C, editor, HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings. Springer Verlag. 2018. p. 515-521. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-92285-0_71

Wu, I. Chin ; Chen, Yi An ; Wang, Zan Xian. / A CDF-based symbolic time-series data mining approach for electricity consumption analysis. HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings. editor / Constantine Stephanidis. Springer Verlag, 2018. pp. 515-521 (Communications in Computer and Information Science).

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title = "A CDF-based symbolic time-series data mining approach for electricity consumption analysis",

abstract = "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 {\textendash} namely, the cumulative distribution function (CDF) {\textendash} 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.",

keywords = "Cumulative distribution function, Electricity consumption analysis, Symbolic aggregate approximation, Time-series data mining",

author = "Wu, \{I. Chin\} and Chen, \{Yi An\} and Wang, \{Zan Xian\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 20th International Conference on HCI, HCI International 2018 ; Conference date: 15-07-2018 Through 20-07-2018",

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PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Cumulative distribution function

KW - Electricity consumption analysis

KW - Symbolic aggregate approximation

KW - Time-series data mining

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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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