A Cloud-Based Energy Monitoring System Using IoT and Machine Learning

  • Zoya Nasroollah
  • Iraiven Moonsamy
  • Yasser ChutturEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)


Finding means to encourage consumers to monitor their energy use is an important step toward optimizing on depleting natural resources used for energy production. The current proposal employs cloud computing and machine learning to analyze energy data collected from a nonintrusive IoT system to display energy consumption from several appliances connected to the same power line. For scalability purpose, all collected data from sensors are processed on the cloud and useful information such as appliance monitored and energy consumed can easily be accessed on a mobile app. Preliminary results indicate that the proposed system promises to be a suitable alternative for traditional monitoring systems to deliver instant and historical energy consumption data to consumers, who can, in turn, adopt efficient and smarter ways to use energy.


IoT Machine learning Energy monitoring Cloud computing 



Special thanks goes to Mr. Y. Beeharry from the University of Mauritius FoICDT computer lab for his valuable advice during the implementation phase of the prototype.


  1. 1.
    Cominola, A., Giuliani, M., Piga, D., Castelletti, A., Rizzoli, A.E.: A hybrid signature-based iterative disaggregation algorithm for non-intrusive load monitoring. Appl. Energy 185, 331–344 (2017)CrossRefGoogle Scholar
  2. 2.
    Basu, K.: Classification techniques for non-intrusive load monitoring and prediction of residential loads. Doctoral dissertation, University of Grenoble (2014)Google Scholar
  3. 3.
    Mashuque, E., Shaikh, R.H., Nafis, A.K., Hafiz, A.R.: Smart energy monitoring using off the-shelf hardware and software tools. In: IASTED International Conference on Power and Energy Systems, pp. 160–167. Thailand (2013)Google Scholar
  4. 4.
    Wood, G., Newborough, M.: Influencing user behaviour with energy information display systems for intelligent homes. J. Energy Res. 39(4), 495–503 (2007)Google Scholar
  5. 5.
    Faruquia, A., Sergici, S., Sharif, A.: The impact of informational feedback on energy consumption—a survey of the experimental evidence. Energy 35(4), 1598–1608 (2010)CrossRefGoogle Scholar
  6. 6.
    Meyers, R.J., Williams, E.D., Matthews, H.S.: Scoping the potential of monitoring and control technologies to reduce energy use in homes. Energy Build. 42(4), 563–569 (2010)CrossRefGoogle Scholar
  7. 7.
    Bonino, D., Corno, F., De Russis, L.: Home energy consumption feedback: a user survey. Energy Build. 47C, 383–393 (2012)CrossRefGoogle Scholar
  8. 8.
    Sundramoorthy, V., Liu, Q., Cooper, G., Linge, N., Cooper, J.: DEHEMS: a user-driven domestic energy monitoring system. In: Internet of Things (IOT), pp. 1–8. Tokyo (2010)Google Scholar
  9. 9.
    Darby, S.: The effectiveness of feedback on energy consumption. Technical Report. Environmental Change Institute, University of Oxford (2006)Google Scholar
  10. 10.
    Noman, A.A., Rahaman, M.F., Ullah, H., Das, R.K.: Android based smart energy meter. In: 4th National Conference on Natural Science and Technology, pp. 1–3. Bangladesh (2017)Google Scholar
  11. 11.
    Mashuque, E., Shaikh, R.H., Nafis, A.K., Hafiz, A.R.: Smart energy monitoring using off the-shelf hardware and software tools. In: IASTED International Conference on Power and Energy Systems, pp. 160–167. Thailand (2013)Google Scholar
  12. 12.
    Fatima, E.B.: Smart home energy management system monitoring and control of appliances using an arduino based network in the context of a micro-grid. Dissertation, Al Akhawayn University, Morocco (2015)Google Scholar
  13. 13.
    Sholahudin, S., Han, H.: Simplified dynamic neural network model to predict heating load of a building using Taguchi method. Energy 115, 1672–1678 (2016)CrossRefGoogle Scholar
  14. 14.
    Tamizharasi, G., Kathiresan, S., Sreenivasan, K.S.: Energy forecasting using artificial neural networks. Energy 3(3), 7568–7576 (2014)Google Scholar
  15. 15.
    Olivencia, P.F., Ferrero, B.J., Gomez, F.J.F., Crespo, M.A.: Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renew. Energy 81, 227–238 (2015)CrossRefGoogle Scholar
  16. 16.
    Chaturvedi, D.K., Sinha, A.P., Malik, O.P.: Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network. Electr. Power Energy Syst. 67, 230–237 (2015)CrossRefGoogle Scholar
  17. 17.
    Kumar, R., Aggarwal, R.K., Sharma, J.D.: Energy analysis of a building using artificial neural network: a review. Energy Build. 65, 352–358 (2013)CrossRefGoogle Scholar
  18. 18.
    Kalogirou, S.A.: Applications of artificial neural networks in energy systems. Energy Convers. Manage. 40, 1073–1087 (1999)CrossRefGoogle Scholar
  19. 19.
    Kushiro, N., Ide, T., Tomonaga, K., Ogawa, Y., Higuma, T.: Can electric devices be identified from their signatures of waveform? In: 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), pp. 531–536. Las Vegas (2015)Google Scholar
  20. 20.
    Fogarty C., Carolyn A., Hudson, S.E.: Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition. In: Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology, pp. 91–100. Switzerland (2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zoya Nasroollah
    • 1
  • Iraiven Moonsamy
    • 1
  • Yasser Chuttur
    • 1
    Email author
  1. 1.University of MauritiusReduitMauritius

Personalised recommendations