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Learning and Prediction of E-Car Charging Requirements for Flexible Loads Shifting

  • Salvatore Venticinque
  • Stefania NacchiaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)

Abstract

The availability of distributed renewable energy sources (RES), such as photo-voltaic panels, allows to locally consume or accumulate energy, avoiding power peaks and loss along the power network. However, as the number of utilities in a household or a building increases, and the energy must be equally and intelligently shared among the utilities and devices, demand side management systems must exploit new solution for allowing such energy usage optimisation. The current trends of demand side management systems highly exploit loads shifting, as a concrete solution to align consumption to the fluctuating produced power, and to maximise the energy utilisation avoiding its wastage. Moreover the introduction, in the latest years, of e-cars has given a boost to smart charging, as it can increase the flexibility that is necessary for maximising the self-consumption. However we strongly believe that a performing demand side management system must be able to learn and predict user’s habits and energy requirements of her e-car, to better schedule the loads shifting and reduce energy wastage. This paper focuses on the e-car utilisation, investigating the exploitation of machine learning techniques to extract and use such knowledge from the power measures at charging plug.

Keywords

Smart energy Energy management system Clustering Machine learning 

References

  1. 1.
    Alizadeh, M., Scaglione, A., Davies, J., Kurani, K.S.: A scalable stochastic model for the electricity demand of electric and plug-in hybrid vehicles. IEEE Trans. Smart Grid 5(2), 848–860 (2014)CrossRefGoogle Scholar
  2. 2.
    Alizadeh, M., Scaglione, A., Wang, Z.: On the impact of smartgrid metering infrastructure on load forecasting. In: 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1628–1636. IEEE (2010)Google Scholar
  3. 3.
    Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  5. 5.
    Jiang, S., Venticinque, S., Horn, G., Hallsteinsen, S., Noebels, M.: A distributed agent-based system for coordinating smart solar-powered microgrids, pp. 71–79 (2016).  https://doi.org/10.1109/SAI.2016.7555964
  6. 6.
    Khan, A.R., Mahmood, A., Safdar, A., Khan, Z.A., Khan, N.A.: Load forecasting, dynamic pricing and dsm in smart grid: a review. Renew. Sustain. Energy Rev. 54, 1311–1322 (2016)CrossRefGoogle Scholar
  7. 7.
    Khodaei, A., Shahidehpour, M., Choi, J.: Optimal hourly scheduling of community-aggregated electricity consumption. J. Electr. Eng. Technol. 8(6), 1251–1260 (2013)CrossRefGoogle Scholar
  8. 8.
    Li, G., Zhang, X.P.: Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations. IEEE Trans. Smart Grid 3(1), 492–499 (2012)CrossRefGoogle Scholar
  9. 9.
    Liang, J., Ng, S.K., Kendall, G., Cheng, J.W.: Load signature study—part I: basic concept, structure, and methodology. IEEE Trans. Power Deliv. 25(2), 551–560 (2010)CrossRefGoogle Scholar
  10. 10.
    Liu, C., Chau, K., Wu, D., Gao, S.: Opportunities and challenges of vehicle-to-home, vehicle-to-vehicle, and vehicle-to-grid technologies. Proc. IEEE 101(11), 2409–2427 (2013)CrossRefGoogle Scholar
  11. 11.
    López, K.L., Gagné, C., Gardner, M.A.: Demand-side management using deep learning for smart charging of electric vehicles. IEEE Trans. Smart Grid 10, 2683–2691 (2018)CrossRefGoogle Scholar
  12. 12.
    López, M., De La Torre, S., Martín, S., Aguado, J.: Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support. Int. J. Electr. Power Energy Syst. 64, 689–698 (2015)CrossRefGoogle Scholar
  13. 13.
    Nagelkerke, N.J., et al.: A note on a general definition of the coefficient of determination. Biometrika 78(3), 691–692 (1991)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Industr. Inf. 7(3), 381–388 (2011)CrossRefGoogle Scholar
  15. 15.
    Pellegrini, M.: Short-term load demand forecasting in smart grids using support vector regression. In: 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 264–268. IEEE (2015)Google Scholar
  16. 16.
    Pellegrini, M., Rassaei, F.: Modeling daily electrical demand in presence of PHEVs in smart grids with supervised learning. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI), pp. 1–6. IEEE (2016)Google Scholar
  17. 17.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefGoogle Scholar
  18. 18.
    Sanseverino, E.R., Di Silvestre, M., Zizzo, G., Graditi, G.: Energy efficient operation in smart grids: optimal management of shiftable loads and storage systems. In: 2012 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), pp. 978–982. IEEE (2012)Google Scholar
  19. 19.
    Xiong, Y., Wang, B., Chu, C.C., Gadh, R.: Electric vehicle driver clustering using statistical model and machine learning. arXiv preprint arXiv:1802.04193 (2018)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Engineering, University of Campania “Luigi Vanvitelli”AversaItaly

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