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IJSTR >> Volume 4 - Issue 12, December 2015 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Clustering Algorithm As A Planning Support Tool For Rural Electrification Optimization

[Full Text]



Ronaldo Pornillosa Parreno Jr, Rowaldo Del Mundo



Index Terms: Benefit Cost Ratio, Centroid, Clustering Algorithm, Cluster Tree, Conceptual Clustering, Distance-based Clustering, Graph Tree,Inconsistent Edge, Kruskal’s Algorithm, Minimal Spanning Tree, MST diameter, Optimization, Routing Index, Rural Electrification



Abstract: In this study clustering algorithm was developed to optimize electrification plans by screening and grouping potential customers to be supplied with electricity. The algorithm provided adifferent approach in clustering problem which combines conceptual and distance-based clustering algorithmsto analyze potential clusters using spanning tree with the shortest possible edge weight and creating final cluster trees based on the test of inconsistency for the edges. The clustering criteria consists of commonly used distance measure with the addition of household information as basis for the ability to pay (ATP) value. The combination of these two parameters resulted to a more significant and realistic clusters since distance measure alone could not take the effect of the household characteristics in screening the most sensible groupings of households. In addition, the implications of varying geographical features were incorporated in the algorithm by using routing index across the locations of the households. This new approach of connecting the households in an area was applied in an actual case study of one village or barangay that was not yet energized. The results of clustering algorithm generated cluster trees which could becomethetheoretical basis for power utilities to plan the initial network arrangement of electrification. Scenario analysis conducted on the two strategies of clustering the households provideddifferent alternatives for the optimization of the cost of electrification. Futhermore,the benefits associated with the two strategies formulated from the two scenarios was evaluated using benefit cost ratio (B/C) to determine which is more economically advantageous. The results of the study showed that clustering algorithm proved to be effective in solving electrification optimization problem and serves its purpose as a planning support tool which can facilitate electrification in rural areas and achieve cost-effectiveness.



[1] R.K. Maskey, “Small-Hydroplants-Based Renewable Power Systems for Remote Regions,” Dissertation, University of Kalsruhe, 2004.

[2] A. Ferligoj,“Recent Developments in Cluster Analysis,”Dissertation, University of Ljubljana

[3] S. Guha , R. Rastogi, K. Shim, “CURE: An Efficient Clustering Algorithm for Large Databases,” Proc. IEEE Conference on Data Engineering, 1998.

[4] M. Halkidi, Y. Batistakis, M. Vazirgiannis, “On Clustering Validation Techniques,”Journal of Intelligent Infomation Systems, vol. 17, n0. 2/3, pp. 107-145, 2001.

[5] S. Epter, M. Krishnamoorthy, M. Zai, “Clusterability Detection and Initial Seed Selection in Large Data Sets,”Unpublished Paper presented to Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY
[6] G. Li., G. Biswas, “Conceptual Clustering with Numeric and Nominal Mixed Data: A New Similarity Based System,”Dissertation, Department of Computer Science, Vanderbilt University, Nashville, TN

[7] E. Lakervi, E. Holmes, Electricity Distribution Network Design, 2nd Ed., Peter Peregrimus Ltd., England, Ch. 3,4,9,14, 1995.

[8] “The Relationship of Regression Line and Centroids,” available at http://illuminations.nctm.org

[9] R. Grimsdale, P. Sinclair, “The design of House-Estate Distribution System using a Digital Computer,”Proc. IEE, vol. 107A, pp. 295-305,1960.

[10] E. Dorado, E. Miguez, “Design of Large Rural-Voltage Networks Using Dynamic Programming Optimization,”IEEE Transcations on Power Systems, vol. 16, no. 4, pp. 898-903, 2001.

[11] M. Carson, G. Cornfield, “Design of Low-Voltage Distribution Networks,”Proc. IEE, vol. 120, no. 5, pp. 585-592, 1973.

[12] L. Evers, “Model-based Clustering, K-means and SOMs,” Sem., University of Oxford,2004.

[13] S. Li, D. Wunsch, E. O’Hair, M. Giesselmann, “Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation,”Journal of Solar Energy Engineering, Vol.123, pp. 327-332, 2001.

[14] H. Liu, L. Yu, “Toward Integrating Feature Selection Algorithm for Classification and Clustering,”Dissertation , Arizona University

[15] C. Monteiro, J. Saraiva, V. Miranda,“Power Plans in Developing Countries-The Solargis Tool,” INESC, Porto, Portugal,Retrieved at http://www.inescn.pt/~lproenca/index.html

[16] S. Raff, “A Perspective on Energy Modeling,”Computers and Operations Research Journal, Pergamon Press, New York, 1975.

[17] W. Stuetzle, “Estimating the Cluster Tree of a Density by Analyzing the Minimal Spanning Tree of a Sample,”Dissertation, University of Washington, 2003.

[18] H. Taha, “Operations Research: An Introduction”, Ch 6, 218, Prentice-Hall, Inc., New Jersey,1998.

[19] V. Valkenburgh , “Basic Electricity”, Ch 1-10, Hayden Books, Indiana, USA, 1985.