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

doi:10.1007/978-3-319-58484-3_35

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 proceeding › Conference 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 language	English
Title of host publication	HCI in Business, Government and Organizations
Subtitle of host publication	Supporting Business - 4th International Conference, HCIBGO 2017 Held as Part of HCI International 2017, Proceedings
Editors	Chuan-Hoo Tan, Fiona Fui-Hoon Nah
Publisher	Springer Verlag
Pages	443-454
Number of pages	12
ISBN (Print)	9783319584836
DOIs	https://doi.org/10.1007/978-3-319-58484-3_35
Publication status	Published - 2017 Jan 1
Externally published	Yes
Event	14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2017 - Espoo, Finland Duration: 2017 Jul 3 → 2017 Jul 6

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10294 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2017
Country	Finland
City	Espoo
Period	2017/07/03 → 2017/07/06

Keywords

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

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-58484-3_35

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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

Analyzing load profiles of electricity consumption by a time series data mining framework. / Wu, I. Chin; Chen, Tzu Li; Chen, Yen Ming; Liu, Tzu Chi; Chen, Yi An.

HCI in Business, Government and Organizations: Supporting Business - 4th International Conference, HCIBGO 2017 Held as Part of HCI International 2017, Proceedings. ed. / Chuan-Hoo Tan; Fiona Fui-Hoon Nah. Springer Verlag, 2017. p. 443-454 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10294 LNCS).

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

Wu, IC, Chen, TL, Chen, YM, Liu, TC & Chen, YA 2017, Analyzing load profiles of electricity consumption by a time series data mining framework. in C-H Tan & FF-H Nah (eds), HCI in Business, Government and Organizations: Supporting Business - 4th International Conference, HCIBGO 2017 Held as Part of HCI International 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10294 LNCS, Springer Verlag, pp. 443-454, 14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2017, Espoo, Finland, 2017/07/03. https://doi.org/10.1007/978-3-319-58484-3_35

Wu IC, Chen TL, Chen YM, Liu TC, Chen YA. Analyzing load profiles of electricity consumption by a time series data mining framework. In Tan C-H, Nah FF-H, editors, HCI in Business, Government and Organizations: Supporting Business - 4th International Conference, HCIBGO 2017 Held as Part of HCI International 2017, Proceedings. Springer Verlag. 2017. p. 443-454. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-58484-3_35

Wu, I. Chin ; Chen, Tzu Li ; Chen, Yen Ming ; Liu, Tzu Chi ; Chen, Yi An. / Analyzing load profiles of electricity consumption by a time series data mining framework. HCI in Business, Government and Organizations: Supporting Business - 4th International Conference, HCIBGO 2017 Held as Part of HCI International 2017, Proceedings. editor / Chuan-Hoo Tan ; Fiona Fui-Hoon Nah. Springer Verlag, 2017. pp. 443-454 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - 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.

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