Advertisement

Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data

  • 7 Accesses

Abstract

Electric consumption forecasting using smart meter dataset is one of the aspects in which machine learning approach is highly applied. Forecasting peak demand and electric appliance consumption requires detailed analysis of smart meter data through classification and clustering methods. Forecasting of electrical appliance and Peak demand is necessary action and a significant part in electric power system planning and development. However, due to variability of household consumption level demand and appliance consumption demand, deep and detail analysis of customers’ smart meter data is required in order to identify critical attributes and the source of variation between the consumption level of appliance, as well as customers demand. This paper focuses on forecasting levels of electric appliance consumption and peak demand with the life style of residential customer’s using data obtained from Irish and Umass repository. Further on, customers life style is analyzed from the results of customer peak demand forecast. Supervised and unsupervised machine learning algorithm called CLARA clustering, support vector machine (SVM) and artificial neural network are applied as in order to achieve forecast the appliance consumption level and peak demand. Mean electric appliance consumption values are calculated from daily, weekly, monthly and total consumption for each appliance from 1 year smart data of 1 min time interval for electric appliance consumption forecasting of individual households. For the customers’ peak demand consumption, only mean of weekly consumption of aggregated households is computed together. The forecasting of customers electric consumption using SVM provides outcome of 99.6% accuracy which is much better than the previous works in the same field of study. The obtained result shows that the implemented methodologies and algorithms are applied at their best level of performance.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. 1.

    Haghi, A., & Toole, O. (2013). The use of smart meter data to forecast electricity demand. CS229 course paper.

  2. 2.

    Kwac, J., Flora, J., & Rajagopal, R. (2014). Household energy consumption segmentation using hourly data. IEEE Transactions on Smart Grid, 5(1), 420–430.

  3. 3.

    Lu, H., Li, B. M., & Wei, H. (2012, June). A small-world of neuronal functional network from multi electrode recordings during a working memory task. In The 2012 international joint conference on neural networks (IJCNN) (pp. 1–6). IEEE.

  4. 4.

    www.ijcaonline.org/archives/volume180/number6/zemene-2017-ijca-916052.pdf.

  5. 5.

    Martinez-Pabon, M., Eveleigh, T., & Tanju, B. (2017). Smart meter data analytics for optimal customer selection in demand response programs. Energy Procedia,107, 49–59.

  6. 6.

    Ali, S., Wu, K., Weston, K., & Marinakis, D. (2016). A machine learning approach to meter placement for power quality estimation in smart grid. IEEE Transactions on Smart Grid,7(3), 1552–1561.

  7. 7.

    Al-Ali, A. R. (2016). Internet of things role in the renewable energy resources. Energy Procedia,100, 34–38.

  8. 8.

    Rodrigues, F., Cardeira, C., & Calado, J. M. F. (2014). The daily and hourly energy consumption and load forecasting using artificial neural network method: A case study using a set of 93 households in Portugal. Energy Procedia,62, 220–229.

  9. 9.

    Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal,1(2), 112–121.

  10. 10.

    Idowu, S., Saguna, S., Åhlund, C., & Schelén, O. (2016). Applied machine learning: Forecasting heat load in the district heating system. Energy and Buildings,133, 478–488.

  11. 11.

    Gajowniczek, K., & Ząbkowski, T. (2014). Short term electricity forecasting using individual smart meter data. Procedia Computer Science,35, 589–597.

  12. 12.

    Yu, W., An, D., Griffith, D., Yang, Q., & Xu, G. (2015). Towards statistical modeling and machine learning based energy usage forecasting in smart grid. ACM SIGAPP Applied Computing Review,15(1), 6–16.

  13. 13.

    Grid UsiGupta, S., Kambli, R., Wagh, S., & Kazi, F. (2015). Support-vector-machine-based proactive cascade prediction in smart grid using a probabilistic framework. IEEE Transactions on Industrial Electronics,62(4), 2478–2486.

  14. 14.

    Shahriar, M. S., & Rahman, M. S. (2015, November). Urban sensing and smart home energy optimization: A machine learning approach. In Proceedings of the 2015 international workshop on internet of things towards applications (pp. 19–22). ACM.

  15. 15.

    Siryani, J., Mazzuchi, T., & Sarkani, S. (2015, March). Framework using Bayesian belief networks for utility effective management and operations. In 2015 IEEE first international conference on big data computing service and applications (BigDataService) (pp. 72–78). IEEE.

  16. 16.

    Walker, D., Creaco, E., Vamvakeridou-Lyroudia, L., Farmani, R., Kapelan, Z., & Savić, D. (2015). Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks. Procedia Engineering,119, 1419–1428.

  17. 17.

    Siryani, J., Tanju, B., & Eveleigh, T. J. (2017). A machine learning decision-support system improvesthe internet of things’ smart meter operations. IEEE Internet of Things Journal,4(4), 1056–1066.

  18. 18.

    Yuce, B., Mourshed, M., & Rezgui, Y. (2017). A smart forecasting approach to district energy management. Energies,10(8), 1073.

  19. 19.

    Alahakoon, D., & Yu, X. (2016). Smart electricity meter data intelligence for future energy systems: A survey. IEEE Transactions on Industrial Informatics,12(1), 425–436.

  20. 20.

    Haben, S., Singleton, C., & Grindrod, P. (2016). Analysis and clustering of residential customers’ energy, behavioral demand using smart meter data. IEEE Transactions on Smart Grid,7(1), 136–144.

  21. 21.

    https://www.kdnuggets.com/2016/07/support-vector-machines-simple-explanation.htm.

  22. 22.

    https://www.packtpub.com/mapt/book/big_data_and_business_intelligence/9781783982103/5/ch05lvl1sc2.

  23. 23.

    Lines, J., Bagnall, A., Caiger-Smith, P., & Anderson, S. (2011, September). Classification of household devices by electricity usage profiles. In International conference on intelligent data engineering and automated learning (pp. 403–412). Berlin, Heidelberg: Springer.

  24. 24.

    Yuce, B., Mourshed, M., & Rezgui, Y. (2017). A smart forecasting approach to district energy management. Energies,10(8), 1073.

  25. 25.

    Zufferey, T., Ulbig, A., Koch, S., & Hug, G. (2016, September). Forecasting of smart meter time series based on neural networks. In International workshop on data analytics for renewable energy integration (pp. 10–21). Cham:Springer.

  26. 26.

    Albert, A., & Rajagopal, R. (2013). Smart meter driven segmentation: What’s your consumption say about you. IEEE Transactions on Power Systems,28(4), 4019–4030.

  27. 27.

    https://www.originenergy.com.au/blog/about-energy/peak-demand-stretching-the-system-to-itslimits.html.

Download references

Acknowledgements

This report summarizes the Master’s thesis written between June 2017 and March 2018 at Symbiosis Institute of Technology, Pune, India. The thesis is part of the 2-year research project “Machine Learning Based Electric consumption classification Analysis using Smart meter data”, by the Department of Computer science and engineering. First of all, I would like to thank to Almighty God for all things by giving strength. And, I am also deeply grateful to Ms Vijayshri Khedkar, my advisor for her support and trust in my work and for her close supervision and constant encouragement and support. I also say thanks to Dr. Swati Ahirraol by giving important ideas, suggestions in my work and her constant encouragement and support. And also Dr. Paula Carroll by giving some clarification related to the ISSDA data set. This work was not possible without the data provided by Irish Social Science Data Archive (ISSDA) and UMass Trace Repository.

Author information

Correspondence to Vijayshri Khedkar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abera, F.Z., Khedkar, V. Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data. Wireless Pers Commun (2020) doi:10.1007/s11277-019-06845-6

Download citation

Keywords

  • CLARA (Clusturing  LARge Application)
  • DR (demand response)
  • Peak demand forecasting
  • Smart meter (smart meter)
  • SVM (support vector machine)