Skip to main content

Cloud Computing for Smart Energy Management (CC-SEM Project)

  • Conference paper
  • First Online:
Smart Cities (ICSC-CITIES 2018)

Abstract

This paper describes the Cloud Computing for Smart Energy Management (CC-SEM) project, a research effort focused on building an integrated platform for smart monitoring, controlling, and planning energy consumption and generation in urban scenarios. The project integrates cutting-edge technologies (Big Data analysis, computational intelligence, Internet of Things, High Performance Computing and Cloud Computing), specific hardware for energy monitoring/controlling built within the project and explores their communication. The proposed platform considers the point of view of both citizens and administrators, providing a set of tools for controlling home devices (for end users), planning/simulating scenarios of energy generation (for energy companies and administrators), and shows some advances in communication infrastructure for transmitting the generated data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://wiki.nilm.eu/datasets.html.

  2. 2.

    https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32.

  3. 3.

    https://www.mmm.ucar.edu/weather-research-and-forecasting-model.

References

  1. Turner, W., Doty, S.: Energy Management Handbook. The Fairmont Press, Lilburn (2007)

    Google Scholar 

  2. Towsend, A.: Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. Ww Norton & Co (2013)

    Google Scholar 

  3. Soares, A., Antunes, C., Oliveira, C., Gomes, A.: A multi-objective genetic approach to domestic load scheduling in an energy management system. Energy 77, 144–152 (2014)

    Article  Google Scholar 

  4. Zakariazadeh, A., Jadid, S., Siano, P.: Economic-environmental energy and reserve scheduling of smart distribution systems: a multiobjective mathematical programming approach. Energy Convers. Manage. 78, 151–164 (2014)

    Article  Google Scholar 

  5. Wahyuddin, Y.: To What extent the grand lyon metropole can harness the smart meter project towards the governance of territorial climate energy plan (PCET) study case: smart electric lyon project initiated by EDF [French Electric Utility Company]. In: International Conference on Public Policy (2017)

    Google Scholar 

  6. Gupta, S., Reynolds, M., Patel, S.: Electrisense: single-point sensing using EMI for electrical event detection and classification in the home. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 139–148 (2010)

    Google Scholar 

  7. Spagnolli, A., et al.: Eco-feedback on the go: motivating energy awareness. Computer 44(5), 38–45 (2011)

    Article  Google Scholar 

  8. Gamberini, L., et al.: Tailoring feedback to users’ actions in a persuasive game for household electricity conservation. In: Bang, M., Ragnemalm, E.L. (eds.) PERSUASIVE 2012. LNCS, vol. 7284, pp. 100–111. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31037-9_9

    Chapter  Google Scholar 

  9. Costanza, E., Ramchurn, S., Jennings, N.: Understanding domestic energy consumption through interactive visualisation: a field study. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225 (2012)

    Google Scholar 

  10. Rabaey, J., Ammer, M., da Silva, J., Patel, D., Roundy, S.: Picoradio supports ad hoc ultra-low power wireless networking. Computer 33(7), 42–48 (2000)

    Article  Google Scholar 

  11. Niyato, D., Xiao, L., Wang, P.: Machine-to-machine communications for home energy management system in smart grid. IEEE Commun. Mag. 49(4), 53–59 (2011)

    Article  Google Scholar 

  12. Karnouskos, S.: The cooperative Internet of Things enabled smart grid. In: 14th IEEE International Symposium on Consumer Electronics, pp. 7–10 (2010)

    Google Scholar 

  13. Orsi, E., Nesmachnow, S.: Iot for smart home energy planning. In: XXIII Congreso Argentino de Ciencias de la Computación (2017)

    Google Scholar 

  14. Orsi, E., Nesmachnow, S.: Smart home energy planning using IoT and the cloud. In: 2017 IEEE URUCON. IEEE (2017)

    Google Scholar 

  15. Kong, W., Dong, Z., Jia, Y., Hill, D., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on lstm recurrent neural network. In: IEEE Transactions on Smart Grid Early Access (2017)

    Google Scholar 

  16. Amarasinghe, K., Marino, D., Manic, M.: Deep neural networks for energy load forecasting. In: IEEE 26th International Symposium on Industrial Electronics, pp. 1483–1488 (2017)

    Google Scholar 

  17. Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository, May 2018

    Google Scholar 

  18. Hong, S.: Individual household electric power consumption, May 2018

    Google Scholar 

  19. Zhou, K., Yang, S.: Understanding household energy consumption behavior: the contribution of energy big data analytics. Renew. Sustain. Energy Rev. 56, 810–819 (2016)

    Article  Google Scholar 

  20. Holmgren, W., Andrews, R., Lorenzo, A., Stein, J.: PVLIB Python 2015. In: 2015 IEEE 42nd Photovoltaic Specialist Conference, pp. 1–5 (2015)

    Google Scholar 

  21. Holmgren, W., Groenendyk, D.: An open source solar power forecasting tool using PVLIB-python. In: 2016 IEEE 43rd Photovoltaic Specialists Conference, pp. 0972–0975 (2016)

    Google Scholar 

  22. IEEE: “Smart cities.” https://smartcities.ieee.org/. Accessed 02 Feb 2019

  23. Ramírez, C.A., Barragán, R.C., García-Torales, G., Larios, V.M.: Low-power device for wireless sensor network for smart cities. In: 2016 IEEE MTT-S Latin America Microwave Conference (LAMC), pp. 1–3, December 2016

    Google Scholar 

  24. Wang, Y.E., et al.: A primer on 3G pp narrowband internet of things. IEEE Commun. Mag. 55, 117–123 (2017)

    Article  Google Scholar 

  25. Yu, C., Yu, L., Wu, Y., He, Y., Lu, Q.: Uplink scheduling and link adaptation for narrowband internet of things systems. IEEE Access 5, 1724–1734 (2017)

    Article  Google Scholar 

Download references

Acknowledgment

CC-SEM project is supported by the STIC-AmSud regional program (France–South America).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Emmanuel Luján , Alejandro Otero , Sebastián Valenzuela , Esteban Mocskos , Luiz Angelo Steffenel or Sergio Nesmachnow .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Luján, E., Otero, A., Valenzuela, S., Mocskos, E., Steffenel, L.A., Nesmachnow, S. (2019). Cloud Computing for Smart Energy Management (CC-SEM Project). In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-CITIES 2018. Communications in Computer and Information Science, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-12804-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-12804-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12803-6

  • Online ISBN: 978-3-030-12804-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics