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Frontiers in Energy

, Volume 12, Issue 3, pp 426–439 | Cite as

Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia

  • Seiya Maki
  • Shuichi Ashina
  • Minoru Fujii
  • Tsuyoshi Fujita
  • Norio Yabe
  • Kenji Uchida
  • Gito Ginting
  • Rizaldi Boer
  • Remi Chandran
Research Article

Abstract

The Paris Agreement calls for maintaining a global temperature less than 2°C above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5°C. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas (GHG) emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system (SEMS) and its deployment in households and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time-series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy-prediction models can be used for low-carbon planning.

Keywords

electricity monitoring electricity demand prediction multiple-variable time-series modeling time-series cluster analysis Indonesia 

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Notes

Acknowledgements

This work was performed under the MOEJ “Innovative Modeling and Monitoring Research toward Low Carbon Society and Eco-Cities and Regions” project.

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Seiya Maki
    • 1
  • Shuichi Ashina
    • 1
  • Minoru Fujii
    • 1
  • Tsuyoshi Fujita
    • 1
  • Norio Yabe
    • 2
  • Kenji Uchida
    • 2
  • Gito Ginting
    • 3
  • Rizaldi Boer
    • 3
  • Remi Chandran
    • 1
  1. 1.National Institute for Environmental StudiesTsukuba city, IbarakiJapan
  2. 2.Fujitsu CompanyChibaJapan
  3. 3.Bogor Agricultural University (IPB)Bogor, West JavaIndonesia

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