Load profiling based consumer categorisation is a procedure that involves stages such as the proper representation of the load data, the selection of one or more pattern recognition algorithms, the assessment of their performance, the formation of daily load curve clusters for each consumer and the formation of the final consumer clusters. This study proposes improvements for every stage, while a new stage is introduced to enhance the whole procedure. All the pattern representation techniques of the load profiling related literature are examined together with the most widely used clustering algorithm, namely the K-means. Two novel modified forms of the algorithm are introduced, specially designed by the authors for the problem under study. The aim is to optimally group together similar daily demand patterns to provide exploitable information to the utilities and the retailers for the efficient design and implementation of innovative energy services, targeted to the different consumer categories.

References

1. 1)
  - S.V. Verdu , M.O. Garcia , C. Senabre , A.G. Marin , F.J.G. Franco . Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Trans. Power Syst. , 4 , 1672 - 1682
2. 2)
  - J.W. Sammon . A nonlinear mapping for data structure analysis. IEEE Trans. Comput. , 5 , 401 - 409
3. 3)
  - 13. Jollife, I.T.: ‘Principal Component Analysis’ (Springer, 1986), 2002, 2ndedn..
4. 4)
  - 7. Li, X., Bowers, C.P., Schnier, T.: ‘Classification of energy consumption in buildings with outlier detection’, IEEE Trans. Ind. Electron., 2010, 57, (11), pp. 3639–3644 (doi: 10.1109/TIE.2009.2027926).
5. 5)
  - 6. Chicco, G.: ‘Overview and performance assessment of the clustering methods for electrical load pattern grouping’, Energy, 2012, 42, (1), pp. 68–80 (doi: 10.1016/j.energy.2011.12.031).
6. 6)
  - G.J. Tsekouras , N.D. Hatziargyriou , E.N. Dialynas . Two-stage pattern recognition of load curves for classification of electricity customers. IEEE Trans. Power Syst. , 1120 - 1128
7. 7)
  - 10. Carpaneto, E., Chicco, G., Napoli, R., Scutariu, M.: ‘Electricity customer classification using frequency-domain load pattern data’, J. Electr. Power Energy Syst., 2006, 28, (1), pp. 13–20 (doi: 10.1016/j.ijepes.2005.08.017).
8. 8)
  - 15. Romanazzi, M.: ‘Influence in canonical variates’, J. Comput. Stat. Data Anal., 1991, 11, (2), pp. 143–164 (doi: 10.1016/0167-9473(91)90066-B).
9. 9)
  - G. Chicco , R. Napoli , F. Piglione . Comparisons among clustering techniques for electricity customer classification. IEEE Trans. Power Syst. , 2 , 933 - 940
10. 10)
  - 18. Ahmad, A., Azmira, I., Ahmad, S., Apandi, N.I.A.: ‘Statistical distributions of load profiling data’. Proc. 2012 IEEE Int. Power Engineering and Optimization Conf., Malaysia, June 2012, pp. 199–203.
11. 11)
  - 2. Union of the Electricity Industry (EUROELECTRIC): ‘Metering, load profiles and settlement in deregulated markets’ (Belgium, 2000), pp. 1–67.
12. 12)
  - 17. Public Power Corporation S.A.: http://www.dei.gr.
13. 13)
  - 12. Petrescu, M., Scutariu, M.: ‘Load diagram charactrisation by means of wavelet packet transform’. Proc. Second Balkan Power Conf., pp. 15–19.
14. 14)
  - 16. Notaristefano, A., Chicco, G., Piglione, F.: ‘Data size reduction with symbolic aggregate approximation for electrical load pattern grouping’, IET Gener. Trans. Distrib., 2013, 7, (2), pp. 108–117 (doi: 10.1049/iet-gtd.2012.0383).
15. 15)
  - 5. Panapakidis, I.P., Alexiadis, M.C., Papagiannis, G.K.: ‘Application of competitive learning clustering in the load time series segmentation’. Proc. 48th Universities’ Power Engineering Conf., Ireland, September 2013, pp. 1–6.
16. 16)
  - 21. Panapakidis, I.P., Alexiadis, M.C., Papagiannis, G.K.: ‘Deriving the optimal number of clusters in the electricity consumer segmentation procedure’. Proc. of the 10th Int. Conf. on the European Energy Market, Sweden, May 2013, pp. 1–8.
17. 17)
  - 9. Verdu, S.V., Garcia, M.O., Franco, F.J.G., et al: ‘Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters’. Proc. IEEE Power Systems Conf. and Exposition, U.S.A., October 2004, pp. 899–906.
18. 18)
  - 8. López, J.J., Aguado, J.A., Martín, F., Munoz, F., Rodríguez, A., Ruiz, J.E.: ‘Hopfield–K-Means clustering algorithm: A proposal for the segmentation of electricity customers’, Electr. Power. Syst. Res., 2011, 81, (2), pp. 716–722 (doi: 10.1016/j.epsr.2010.10.036).
19. 19)
  - 19. Zou, G.Y., Taleban, J., Huo, C.Y.: ‘Confidence interval estimation for lognormal data with application to health economics’, J. Comp. Stat. Data Anal., 2009, 53, (11), pp. 3755–3764 (doi: 10.1016/j.csda.2009.03.016).
20. 20)
  - 20. Panapakidis, I.P., Alexiadis, M.C., Papagiannis, G.K.: ‘Load profiling in the deregulated electricity markets: A review of the applications’. Proc. of the Ninth Int. Conf. on the European Energy Market, Italy, May 2012, pp. 1–8.
21. 21)
  - 1. Wang, W., Xu, Y., Khanna, M.: ‘A survey on the communication architectures in smart grid’, Comput. Netw., 2011, 55, (15), pp. 3604–3629 (doi: 10.1016/j.comnet.2011.07.010).

Enhancing the clustering process in the category model load profiling

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