ELECTRIC LOAD PATTERN CLASSIFICATION USING PARAMETER ESTIMATION, CLUSTERING AND ARTIFICIAL NEURAL NETWORKS

Jaime Buitrago, Ahmed Abdulaal, and Shihab Asfour

References

  1. [1] U.S. Department of Energy, Information Administration,Independent Statistics and Analysis. (2015). Monthly/annualenergy review – electricity section. Retrieved from http://www.ei.gov/totalenergy/.
  2. [2] G. Barbose, C. Goldman, and B. Neenan, Electricity in real time – a survey of utility experience with real time pricing, Energy (Norwalk, Connecticut), 30(1), 2005, 14–18.
  3. [3] Y. Ji Hoon, R. Baldick, and A. Novoselac, Dynamic demand response controller based on real-time retail price for residential buildings, IEEE Transactions on Smart Grid, 5(1), 2014, pp. 121–129.
  4. [4] A.H. Mohsenian-Rad and A. Leon-Garcia, Optimal residential load control with price prediction in real-time electricity pricing environments, IEEE Transactions on Smart Grid, 1(2), 2010, 120–133.
  5. [5] A.J. Roscoe and G. Ault, Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response, IET Renewable Power Generation, 4(4), 2010, 369–382.
  6. [6] P. Samadi, H. Mohsenian-Rad, V.W.S. Wong, and R. Schober, Real-time pricing for demand response based on stochastic approximation, IEEE Transactions on Smart Grid, 5(2), 2014, 789–798.
  7. [7] S.M. Bidoki, N. Mahmoudi-Kohan, and S. Gerami, Compar-ison of several clustering methods in the case of electrical load curves classification, 2011 16th Conference on Electrical Power Distribution Networks (EPDC), Bandar Abbas, Iran, Conference Proceedings, 2011, 1–7.
  8. [8] S.M. Bidoki, N. Mahmoudi-Kohan, M.H. Sadreddini,M. Zolghadri Jahromi, and M.P. Moghaddam, Evaluatingdifferent clustering techniques for electricity customer classification, 2010 IEEE PES Transmission and Distribution Conference and Exposition, New Orleans, LA, USA, ConferenceProceedings, 2010, 1–5.
  9. [9] P.T.T. Binh and L.D. Tuong, Clustering the behaviour ofelectricity consumption, IPEC, 2012 Conference on Power andEnergy, Ho Chi Minh City, Vietnam, Conference Proceedings,2002, 402–406.
  10. [10] G. Chicco, R. Napoli, and F. Piglione, Application of clustering algorithms and self organising maps to classify electricity customers, Power Tech Conference Proceedings, 2003 IEEE Bologna, vol. 1, Conference Proceedings, 2003, 7.
  11. [11] G. Chicco, R. Napoli, and F. Piglione, Comparisons among clustering techniques for electricity customer classification, IEEE Transactions on Power Systems, 21(2), 2006, 933–940.
  12. [12] G. Chicco, Overview and performance assessment of the clustering methods for electrical load pattern grouping, Energy, 42(1), 2012, 68–80.
  13. [13] Y. In Hyeob, L. Jin Ki, K. Jong Min, and K. Sun Ic, A method for classification of electricity demands using load profile data, 2005 Fourth Annual ACIS International Conference on Computer and Information Science, Jeju Island, South Korea, Conference Proceedings, 2005, 164–168.
  14. [14] F. McLoughlin, A. Duffy, and M. Conlon, A clustering approach to domestic electricity load profile characterisation using smart metering data, Applied Energy, Volume 141, 2015, 190–199.
  15. [15] V. Ford and A. Siraj, Clustering of smart meter data for disaggregation, 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Austin, TX, USA, Conference Proceedings, 2013, 507–510.
  16. [16] L. Hern´andez, C. Baladr´on, J. Aguiar, B. Carro, andA. S´anchez-Esguevillas, Classification and clustering of electricity demand patterns in industrial parks, Energies, 5(12), 2012, 5215–5228.
  17. [17] T. Warren Liao, Clustering of time series data: A survey, Pattern Recognition, 38(11), 2005, 1857–1874.
  18. [18] J.D. Rhodes, W.J. Cole, C.R. Upshaw, T.F. Edgar, and M.E. Webber, Clustering analysis of residential electricity demand profiles, Applied Energy, Volume 135, pp. 461–471, 2014.
  19. [19] R. Granell, C.J. Axon, and D.C.H. Wallom, Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles, IEEE Transactions on Power Systems, 30(6), 2015, 3217–3224.
  20. [20] P. Saehong, R. Seunghyoung, C. Yohwan, and K. Hongseok, A framework for baseline load estimation in demand response: Data mining approach, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy, Conference Proceedings, 2014, 638–643.
  21. [21] C.M. Bishop, Neural networks for pattern recognition (Berlin: Springer Science + Business Media, 2006).
  22. [22] J.C. Lagarias, J.A. Reeds, M.H. Wright, and P.E. Wright, Convergence properties of the Nelder–Mead simplex method in low dimensions, SIAM Journal on Optimization, 9(1), 1998, 112–36.
  23. [23] D. Arthur and S. Vassilvitskii, k-means++: The advantages of careful seeding, 2007, Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, Louisiana, LA, 1027–1035.
  24. [24] A.K. Jain, M.N. Murty, and P.J. Flynn, Data clustering: A review, ACM Computing Surveys, 31(3), 1999, 264–323.
  25. [25] S. Karsoliya, Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture, International Journal of Engineering Trends and Technology, 3(6), 2012, 713–717.
  26. [26] A. Anastopoulou, I. Koutsopoulos, and G.D. Stamoulis,Efficient incentive-driven consumption curtailment mechanismsin Nega-Watt markets, 2014 IEEE International Conferenceon Smart Grid Communications, Venice, Italy, ConferenceProceedings, 2014, 734–739.

Important Links:

Go Back