Abstract
Electrical Load Pattern Shape (LPS) clustering of customers is an important part of the tariff formulation process. Nevertheless, the patterns describing the energy consumption of a customer have some characteristics (e.g., a high number of features corresponding to time series reflecting the measurements of a typical day) that make their analysis different from other pattern recognition applications. In this paper, we propose a clustering algorithm based on ant colony optimization (ACO) to solve the LPS clustering problem. We use four well-known clustering metrics (i.e., CDI, SI, DEV and CONN), showing that the selection of a clustering quality metric plays an important role in the LPS clustering problem. Also, we compare our LPS-ACO algorithm with traditional algorithms, such as k-means and single-linkage, and a state-of-the-art Electrical Pattern Ant Colony Clustering (EPACC) algorithm designed for this task. Our results show that LPS-ACO performs remarkably well using any of the metrics presented here.
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Notes
- 1.
For simplicity, in this work only the first 250 LPSs were considered from the available data on-line: http://www.ucd.ie/issda/data/commissionforenergyregulationcer.
- 2.
SI metric was not taken into account in the parameter tuning since its behavior is similar to the results obtained with CDI metric.
- 3.
The set of parameters used for the EPACC algorithm were those reported as the best set of parameters in [1].
References
Chicco, G., Ionel, O.M., Porumb, R.: Electrical load pattern grouping based on centroid model with ant colony clustering. IEEE Trans. Power Syst. 28(2), 1706–1715 (2013)
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38(1), 218–237 (2008)
Chicco, G., Napoli, R., Piglione, F.: Comparisons among clustering techniques for electricity customer classification. IEEE Trans. Power Syst. 21(2), 933–940 (2006)
Figueiredo, V., Rodrigues, F., Vale, Z., Gouveia, J.: An electric energy consumer characterization framework based on data mining techniques. IEEE Trans. Power Syst. 20(2), 596–602 (2005)
Chicco, G., Ionel, O.M., Porumb, R.: Formation of load pattern clusters exploiting ant colony clustering principles. In: IEEE EUROCON, pp. 1460–1467 (2013)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 26(1), 29–41 (1996)
Gerbec, D., Gasperic, S., Smon, I., Gubina, F.: Allocation of the load profiles to consumers using probabilistic neural networks. IEEE Trans. Power Syst. 20(2), 548–555 (2005)
Chaouch, M.: Clustering-based improvement of nonparametric functional time series forecasting: application to intra-day household-level load curves. IEEE Trans. Smart Grid 5(1), 411–419 (2014)
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Li, M., Ming-ming, S.: An improved ant colony clustering algorithm based on dynamic neighborhood. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 1, pp. 730–734 (2010)
Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95–104 (1974)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979)
Chou, C.H., Su, M.C., Lai, E.: A new cluster validity measure and its application to image compression. Pattern Anal. Appl. 7(2), 205–220 (2004)
Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(2), 1161–1172 (2004)
Lloyd, S.: Least squares quantization in pcm. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)
Day, W.H., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1(1), 7–24 (1984)
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Lezama, F., Rodríguez, A.Y., de Cote, E.M., Sucar, L.E. (2016). Electrical Load Pattern Shape Clustering Using Ant Colony Optimization. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_32
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DOI: https://doi.org/10.1007/978-3-319-31204-0_32
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