Automatic Detection of Device Types by Consumption Curve

  • Claudio TomazzoliEmail author
  • Matteo Cristani
  • Simone Scannapieco
  • Francesco Olivieri
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 96)


This work deals with the problem of automatic detection of device types given only the power consumption curve, which can be obtained by means of a cheap measurer applied to the device itself. We defined a novel method to detect these types and we describe it in details, providing ground truth evidence coming from the application of the method to real world data. We tested the method against two different set of data coming from two separate and different environments, the first located in Italy and the second in Germany, and we provide experimental results to support the method.


  1. 1.
    Anvari-Moghaddam, A., Monsef, H., Rahimi-Kian, A.: Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans. Smart Grid 6(1), 324–332 (2015)CrossRefGoogle Scholar
  2. 2.
    Baliga, J., Ayre, R., Hinton, K., Sorin, W.V., Tucker, R.S.: Energy consumption in optical IP networks. J. Lightwave Technol. 27(13), 2391–2403 (2009)CrossRefGoogle Scholar
  3. 3.
    Baliga, J., Hinton, K., Ayre, R., Tucker, R.S.: Carbon footprint of the internet (2009)CrossRefGoogle Scholar
  4. 4.
    Bicego, M., Farinelli, A., Grosso, E., Paolini, D., Ramchurn, S.D.: On the distinctiveness of the electricity load profile. Pattern Recognit. 74, 317–325 (2018)CrossRefGoogle Scholar
  5. 5.
    Bicego, M., Recchia, F., Farinelli, A., Ramchurn, S.D., Grosso, E.: Behavioural biometrics using electricity load profiles, pp. 1764–1769 (2014)Google Scholar
  6. 6.
    Breheny, M.: The compact city and transport energy consumption. Trans. Inst. Br. Geogr. 20(1), 81–101 (1995)CrossRefGoogle Scholar
  7. 7.
    Cristani, M., Karafili, E., Tomazzoli, C.: An ambient intelligence technology for energy saving. In: IEA-AIE 2014 Proceedings. Springer (2014)Google Scholar
  8. 8.
    Cristani, M., Karafili, E., Tomazzoli, C.: Energy saving by ambient intelligence techniques. In: 2014 17th International Conference on Network-Based Information Systems (NBiS), pp. 157–164. IEEE (2014)Google Scholar
  9. 9.
    Cristani, M., Karafili, E., Tomazzoli, C.: Improving energy saving techniques by ambient intelligence scheduling. In: Proceedings of the 2015 IEEE 29th International Conference on Advanced Information Networking and Applications (AINA 2015), vol. 1, pp. 324–331, Los Alamitos, California, Conference Publishing Services (CPS) – IEEE Computer Society (2015)Google Scholar
  10. 10.
    Cristani, M., Tomazzoli, C., Olivieri, F., Karafili E.: Defeasible reasoning about electric consumptions. In: Proceedings of the 30th IEEE International Conference on Advanced Information Networking and Applications (AINA-2016), pp. 885–892 (2016)Google Scholar
  11. 11.
    Fan, X., Weber, W-.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th Annual International Symposium on Computer Architecture, ISCA 2007, pp. 13–23. ACM, New York (2007)Google Scholar
  12. 12.
    Governatori, G., Olivieri, F., Rotolo, A., Scannapieco, S., Cristani, M.: Picking up the best goal an analytical study in defeasible logic. LNCS (LNAI and LNBI), vol. 8035, pp. 99–113 (2013)CrossRefGoogle Scholar
  13. 13.
    Governatori, G., Olivieri, F., Scannapieco, S., Cristani, M.: Designing for compliance: norms and goals. LNCS (LNAI and LNBI), vol. 7018, pp. 282–297 (2011)CrossRefGoogle Scholar
  14. 14.
    Governatori, G., Olivieri, F., Scannapieco, S., Rotolo, A., Cristani, M.: The rationale behind the concept of goal. Theor. Pract. Logic Program. 16(3), 296–324 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    The Climate Group: Smart 2020: Enabling the low carbon economy in the information age (2008)Google Scholar
  16. 16.
    Hopf, K., Sodenkamp, M., Kozlovkiy, I., Staake, T.: Feature extraction and filtering for household classification based on smart electricity meter data. Comput. Sci. Res. Dev. 31(3), 141–148 (2016)CrossRefGoogle Scholar
  17. 17.
    Olivieri, F., Governatori, G., Scannapieco, S., Cristani, M.: Compliant business process design by declarative specifications. LNCS (LNAI and LNBI), vol. 8291, pp. 213–228 (2013)Google Scholar
  18. 18.
    Prez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2008)CrossRefGoogle Scholar
  19. 19.
    Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C., Argiriou, A., Assimakopoulos, D.N.: On the impact of urban climate on the energy consumption of buildings. Sol. Energy 70(3), 201–216 (2001). Urban EnvironmentCrossRefGoogle Scholar
  20. 20.
    Scannapieco, S., Tomazzoli, C.: Ubiquitous and pervasive computing for real-time energy management and saving. In: Barolli, L., Enokido, T. (eds.) Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 3–15. Springer International Publishing, Cham (2018)Google Scholar
  21. 21.
    Tomazzoli, C., Cristani, M., Karafili, E., Olivieri, F.: Non-monotonic reasoning rules for energy efficiency. J. Ambient Intell. Smart Environ. 9, 345–360 (2017)CrossRefGoogle Scholar
  22. 22.
    Tomazzoli, C., Cristani, M., Olivieri, F.: Automatic synthesis of best practices for energy consumptions. In: Proceedings of the Tenth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 1–8. IEEE CPS (2016)Google Scholar
  23. 23.
    Tucker, R.S., Parthiban, R., Baliga, J., Hinton, K., Ayre, R.W.A., Sorin, W.V.: Evolution of WDM optical IP networks: a cost and energy perspective. J. Lightwave Technol. 27(3), 243–252 (2009)CrossRefGoogle Scholar
  24. 24.
    Valogianni, K., Ketter, W., Collins, J., Zhdanov, D.: Enabling sustainable smart homes: an intelligent agent approach (2014)Google Scholar
  25. 25.
    Vastamäki, R., Sinkkonen, I., Leinonen, C.: A behavioural model of temperature controller usage and energy saving. Pers. Ubiquitous Comput. 9(4), 250–259 (2005)CrossRefGoogle Scholar
  26. 26.
    Wood, G., Newborough, M.: Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design. Energy Build. 35(8), 821–841 (2003)CrossRefGoogle Scholar
  27. 27.
    Xu, Z., Jia, Q-.S., Guan, X., Xie, X.: A new method to solve large-scale building energy management for energy saving. In: 2014 IEEE International Conference on Automation Science and Engineering, CASE 2014, New Taipei, Taiwan, 18–22 August 2014, pp. 940–945. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Claudio Tomazzoli
    • 1
    Email author
  • Matteo Cristani
    • 1
  • Simone Scannapieco
    • 2
  • Francesco Olivieri
    • 3
  1. 1.Dipartimento di InformaticaUniversità di VeronaVeronaItaly
  2. 2.R&D DepartmentReal TVeronaItaly
  3. 3.Data61BrisbaneAustralia

Personalised recommendations