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Simulation of Energy Management by Controlling Crowd Behavior

  • Maiya HoriEmail author
  • Keita Nakayama
  • Atsushi Shimada
  • Rin-ichiro Taniguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10921)

Abstract

We propose a method of energy management aimed at reducing the emission of carbon dioxide by changing people’s behavior in small and medium-sized electricity communities. In the conventional energy management system, a power peak is cut and shifted mainly using solar power generation and batteries. In this research, a power peak is cut and shifted by controlling the power demand. The power demand for each facility in small communities is controlled by changing crowd behavior. In experiments, models for predicting power demand according to crowd congestion are constructed for each facility and the accuracies of prediction are verified.

Keywords

Energy management system Power demand Crowd behavior 

Notes

Acknowledgements

This research was supported by the Japan Science and Technology Agency (JST) through its Center of Innovation: Science and Technology Based Radical Innovation and Entrepreneurship Program (COI STREAM).

References

  1. 1.
    Behl, M., Jain, A., Mangharam, R.: Data-driven modeling, control and tools for cyber-physical energy systems. In: ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) (2016)Google Scholar
  2. 2.
    Ministry of the Envirnment, Government of Japan. Mid-and long-term roadmap for global warming measures. https://funtoshare.env.go.jp/roadmap/index_en.html
  3. 3.
    Barbato, A., Delfanti, M., Bolchini, C., Geronazzo, A., Quintarelli, E., Olivieri, V., Rottondi, C., Verticale, G.: An energy management framework for optimal demand response in a smart campus. In: International Conference on Green IT Solutions (ICGREEN) (2015)Google Scholar
  4. 4.
    Siano, P.: Demand response and smart grids - a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2013)CrossRefGoogle Scholar
  5. 5.
    Deng, R., Yang, Z., Chow, M., Chen, J.: A survey on demand response in smart grids: Mathematical models and approaches. IEEE Trans. Ind. Inform. 11(3), 570–582 (2015)CrossRefGoogle Scholar
  6. 6.
    Hori, M., Goto, T., Takano, S., Taniguchi, R.: Power demand forecasting using meteorological data and human congestion information. In IEEE International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA) (2016)Google Scholar
  7. 7.
    Japan Meteorological Agency. Weather, climate & earthquake information. http://www.jma.go.jp/jma/indexe.html
  8. 8.
    Fod, A., Howard, A., Mataric, M.A.J.: A laser-based people tracker. In: IEEE International Conference on Robotics and Automation (ICRA) (2002)Google Scholar
  9. 9.
    Akaike, H., Nakagawa, T.: Statistical Analysis and Control of Dynamic Systems. Springer, Heidelberg (1988). ISBN 978-90-277-2786-2zbMATHGoogle Scholar
  10. 10.
    Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)
  11. 11.
    Seijen, H., Fatemi, M., Romoff, J., Laroche, R., Barnes, T., Tsang, J.: Hybrid reward architecture for reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 5398–5408 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Maiya Hori
    • 1
    Email author
  • Keita Nakayama
    • 1
  • Atsushi Shimada
    • 1
  • Rin-ichiro Taniguchi
    • 1
  1. 1.Kyushu UniversityNishi-kuJapan

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