Efficient methods for energy saving in smart homes through breakdown the energy consumption

  • Authors

    • Mayank singh University of KwaZulu-Natal, Durban, South Africa
    • Viranjay M. Srivastava University of KwaZulu-Natal, Durban, South Africa
    2019-02-25
    https://doi.org/10.14419/ijet.v7i4.10173
  • Energy Consumption, Smart Home, Domestic Appliances, Switched-Mode Power Supply, Energy Reliably, Energy Efficiency.

  • Abstract

    The most crucial issue that affects the consumers, and environment is energy saving. Various home appliances are the primary con-cern in energy savings due to high-energy demand by these appliances. Smart home technologies are a good alternative for energy saving, and comfort for household devices. Buildings (structure and window direction) contribute significantly to overall energy consumption. Studies suggest that providing occupants with energy breakdown per-appliance energy consumption can help to save nearly 15% of energy. However, there are weak practical solutions are present to provide an energy saving breakdown. Current re-search requires hardware in each home and thus cannot be scaled across all households. In this research work, we have presented the techniques for producing energy breakdown in a home without requiring any additional sensing. Therefore, to identify the ener-gy breakdown, we have categorized the appliances in the different classes, i.e., switched-mode based power supply (SMPS), purely resistive, thermostatically controlled, etc. We are proposing a technique for the development of smart appliances that incorporate actuation capabilities and local intelligence for optimal appliance operation. With this proposed approach, the control and knowledge are pushed increasingly to the end device. This proposed approach has been applied to 287 homes from the data set available publically, in which the energy consumption measurement is based on individual home appliances. We have analysed the learned potential factors and found that the energy breakdown performance can improve using the static household properties. These proposed methods are scalable and approximately 42% more accurate compared to the state-of-the-art energy breakdown techniques.

     

    Author Biography

    • Mayank singh, University of KwaZulu-Natal, Durban, South Africa
      Software Engineering, Software Testing, Cloud Computing, Internet of Things, Ubiquitous Computing, BioMedical Engineering

  • References

    1. [1] Mark Ellis, “Gadget and gigawatts – policies for energy efficient electronics,†International Energy Agency, 2009.

      [2] B. Cook, J. Gazzano, Z. Gunay, L. Hiller, S. Mahajan, A. Taskan, and S. Vilogorac, “The smart meter and a smarter consumer: quantifying the benefits of smart meter implementation in the United States,†Chemistry Central Journal, Springer, vol 6, no.1, 2012.

      [3] International energy outlook 2016, Energy Information and Administration, 2016.

      [4] Y. Guo, M. Jones, B. Cowan, and R. Beale, “Take it personally: personal accountability and energy consumption in domestic households,†Proceeding of CHI '13 Extended Abstracts on Human Factors in Computing Systems, Paris, France, pp. 1467-1472, May 2013.

      [5] C. Fischer, “Feedback on household electricity consumption: a tool for saving energy?†Energy Efficiency, Springer, vol. 1, no. 1, pp. 79-104, Feb 2008. https://doi.org/10.1007/s12053-008-9009-7.

      [6] M. E. Berges, E. Goldman, H. S. Matthews, and L. Soibelman, “Enhancing electricity audits in residential buildings with nonintrusive load monitoring,†Journal of Industrial Ecology, Wiley, vol. 14, no. 5, 2010.

      [7] J. Z. Kolter, and T. Jaakkola, “Approximate interface in additive factorial HMMs with application to energy disaggregation,†Proceeding of 15th International Conference on Artificial Intelligence and statistics, La Palma, Canary Islands, pp. 1472-1482, 2012.

      [8] O. Parson, S. Ghosh, M. Weal, and A. Rogers, " Non-intrusive load monitoring prior models of general appliance types,†Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, Canada, pp. 356-362, July 2012.

      [9] K. C. Armel, A. Gupta, S. Shrimali, and A. Albert, “Is disaggregation the holy grail of energy efficiency? The case of electricity,†Energy Policy, Elsevier, vol. 52, pp. 213-234, Jan 2013. https://doi.org/10.1016/j.enpol.2012.08.062.

      [10] X. Jiang, S. D. Haggerty, P. Dutta, and D. Culler, “Design and implementation of a high-fidelity AC metering network,†Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, Washington, USA, pp. 253-264, Apr 2009.

      [11] S. Debruin, B. Ghena, Y.S. Kuo, and P. Dutta, “PowerBlade: A Low-Profile, True-Power, Plug-Through Energy Meter,†Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, South Korea, pp. 17-29, Nov 2015. https://doi.org/10.1145/2809695.2809716.

      [12] T.W. Hnat, V. Srinivasn, J. Lu, T.I. Sookoor, R. Dawson, J. Stankovic, and K. Whitehouse, “The hitchhiker's guide to successful residential sensing deployments,†Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, Seattle, USA, pp. 232-245, Nov 2011. https://doi.org/10.1145/2070942.2070966.

      [13] M. Clark, B. Campbell, and P. Dutta, “Deltaflow: submetering by synthesizing uncalibrated pulse sensor streams,†Proceedings of the 5th international conference on Future energy systems, Cambridge, United Kingdom, pp. 301-311, Jun 2014. https://doi.org/10.1145/2602044.2602070.

      [14] M. Jain, A. Singh, and V. Chauhan, “Non-intrusive estimation and prediction of residential AC energy consumption,†Proceeding of 2016 IEEE International Conference on Pervasive Computing and Communications, Sydney, Australia, pp. 1-9, Mar 2016. https://doi.org/10.1109/PERCOM.2016.7456509.

      [15] S. Gupta, M.S. Reynolds, and S. N. Patel, “ElectriSense: single point sensing using EMI for electrical event detection and classification in the home,†Proceeding of 12th ACM International Conference on Ubiquitous Computing, Copenhagen, Denmark, pp. 139-148, Sep 2010. https://doi.org/10.1145/1864349.1864375.

      [16] K.Y. Chen, S. Gupta, E.C. Larson, and S. Patel, “DOSE: Detecting user-driven operating states of electronic devices from a single sensing point,†Proceeding of 2015 IEEE International Conference on Pervasive Computing and Communications, St. Louis, USA, pp. 46-54, Mar 2015. https://doi.org/10.1109/PERCOM.2015.7146508.

      [17] D. Christensen, L. Earle, and B. Sparn, “NILM applications for the energy efficient home,†National Renewable Energy Laboratory, Technical Report, Nov 2012.

      [18] A. Zoha, A. Gluhak, M.A. Imran, and S. Rajasegarar, “Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey,†Sensor Journal, MDPI, pp. 16838-16866, Dec 2012. https://doi.org/10.3390/s121216838.

      [19] M. Hazas, A. Friday, and J. Scott, “Loook back before leaping forward: four decades of domestic energy inquiry,†IEEE Pervasive Computing, vol. 10, no. 1, pp. 13-19, Mar 2011. https://doi.org/10.1109/MPRV.2010.89.

      [20] G.W. Hart, “Nonintrusive appliance load monitoring,†Proceedings of the IEEE, vol. 80, no. 12, pp. 1870-1891, Dec 1992. https://doi.org/10.1109/5.192069.

      [21] M. Weiss, A. Helfenstein, F. Mattern and T. Staake, "Leveraging smart meter data to recognize home appliances," 2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, pp. 190-197, 2012. https://doi.org/10.1109/PerCom.2012.6199866.

      [22] The smart meter revolution, Technical Report, Telefonica, Jan 2014.

      [23] K. Tweed, “Bidgley raises $16 million for energy disaggregation,†Energy Management, Nov 2015.

      [24] Energy statistics 2017, Ministry of Statistics and Programme Implementation, Government of India, 2017.

      [25] Household consumption of various goods and services in India, National Sample Survey Office, Ministry of Statistics and Programme Implementation, Government of India, Jun 2014.

      [26] S. Dharmadhikary, and R. Bhalerao, “How much energy do we need: towards end-use based estimation for decent living,†Energy Resource and Development, May 2015.

      [27] Mayank Singh, and Viranjay M. Srivastava, “Cloud-Based Predictive Intelligence and Its Security Model,†Predictive Intelligence Using Big Data and the Internet of Things,IGI Global, USA, pp. 128-143, 2019. https://doi.org/10.4018/978-1-5225-6210-8.ch006.

      [28] Mayank Singh, and Viranjay M Srivastava, “Multiple Regression Based Cloud Adoption Factors for Online Firms,†Proceedings of 4th IEEE International Conference on Advances in Computing and Communication Engineering (ICACCE 2018 #3), Paris, France, pp. 147-152, June 2018. https://doi.org/10.1109/ICACCE.2018.8457722

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  • How to Cite

    singh, M., & M. Srivastava, V. (2019). Efficient methods for energy saving in smart homes through breakdown the energy consumption. International Journal of Engineering & Technology, 7(4), 4780-4784. https://doi.org/10.14419/ijet.v7i4.10173

    Received date: 2018-03-15

    Accepted date: 2018-05-24

    Published date: 2019-02-25