JACIII Vol.24 No.2 pp. 214-220
doi: 10.20965/jaciii.2020.p0214


Lifestyle Analysis from Household Electricity Consumption Data

Yu Shirai*, Shunichi Hattori**, and Yasufumi Takama*

*Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

**Central Research Institute of Electric Power Industry
2-11-1 Iwadokita, Komae, Tokyo 201-8511, Japan

February 20, 2019
January 27, 2020
March 20, 2020
electricity consumption data, smart meter, lifestyle analysis

This paper aims to analyze the lifestyle of residents from household electricity consumption data. Improving QOL (Quality of Life) of elderlies has attracted attention in a super-aging society. It is known that the lifestyle of a person directly affects his / her health and QOL. Therefore, understanding a lifestyle is expected to be useful for providing various support for improving QOL, such as recommending adequate actions and daily habit. As a means for understanding residents’ lifestyle, this paper focuses on household electricity consumption data, which gets to be available with the spread of smart meters. The analysis is conducted by estimating the time of taking essential actions such as wake up and eating. As the target data has no ground truth, this paper also shows the result of an experiment on the detection of the essential actions. The analysis results reveal several findings which could be useful for improving QOL, such as positive correlation between regularity of dinner time and bedtime.

The process from inputting data to clustering

The process from inputting data to clustering

Cite this article as:
Y. Shirai, S. Hattori, and Y. Takama, “Lifestyle Analysis from Household Electricity Consumption Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.2, pp. 214-220, 2020.
Data files:
  1. [1] B. Spilker, “Quality of life and pharmacoeconomics in clinical trials,” Second Edition, Lippincott Williams & Wilkins, 1996.
  2. [2] L. W. Green and M. W. Kreuter, “Health promotion planning: an educational and environmental approach,” Mayfield Pub. Co., 1991.
  3. [3] R. L. Thorndike, “Who belongs in the family?,” Psychometrika, Vol.18, No.4, pp. 267-276, 1953.
  4. [4] W. Kleiminger, C. Beckel, and S. Santini, “Household occupancy monitoring using electricity meters,” Proc. of the 2015 ACM Int. Joint Conf. and 2015 Int. Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 975-986, doi: 10.1145/2750858.2807538, 2015.
  5. [5] V. Beckera and W. Kleiminger, “Exploring zero-training algorithms for occupancy detection based on smart meter measurements,” Computer Science – Research and Development, Vol.33, No.1-2, pp. 25-36, doi: 10.1007/s00450-017-0344-9, 2018.
  6. [6] S. Haben, C. Singleton, and P. Grindrod, “Analysis and clustering of residential customers energy behavioral demand using smart meter data,” IEEE Trans. on Smart Grid, Vol.7, No.1, doi: 10.1109/TSG.2015.2409786, 2015.
  7. [7] Y. Shirai, S. Hattori, and Y. Takama, “Analyzing resident’s lifestyle from household electricity consumption data,” Proc. of The 8th Int. Symp. on Computational Intelligence and Industrial Applications (ISCIIA 2018), Session No.3A2-2-2, 2018.
  8. [8] D. Tomikoshi, T. Ikaga, S. Kawakubo, and K. Fujisaki, “Development of a suggestion tool for energy-saving actions based on the analysis of residents’ behaviors and energy consumption,” AIJ J. of Technology and Design, Vol.19, No.42, pp. 655-660, doi: 10.3130/aijt.19.655, 2013 (in Japanese).
  9. [9] C. Sekine, Y. Watanabe, and M. Hayashida, “Decrease in Sleeping Time Stopped,” Time Spent for Necessary Activities Increased: from the 2015 NHK Japanese time use survey, NHK (Japan Broadcasting Corporation), 2016, [accessed October 13, 2019]
  10. [10] S. Bureau, “Ministry of internal affairs and communications,” Survey on Time Use and Leisure Activities, 2011, (in Japanese) [accessed February 3, 2018]
  11. [11] S. E. Page, “Continuous inspection schemes,” Biometrika, Vol.41, No.1-2, pp. 100-226, doi: 10.2307/2333009, 1954.
  12. [12] C. J. Deepu, Z. Chen, J. T. Teo, S. H. Ng, X. Yang, and Y. Lian, “A smart cushion for real-time heart rate monitoring,” Proc. of the IEEE Biomedical Circuits and Systems Conf., pp. 53-56, doi: 10.1109/BioCAS.2012.6418512, 2012
  13. [13] D. Arthur, “k-means++: The advantages of careful seeding,” Proc. of the 18th Annual ACM-SIAM Symp. on Discrete Algorithms, pp. 1027-1035, doi: 10.1145/1283383.1283494, 2007.

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Last updated on Jul. 19, 2024