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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]

 

AUTHOR(S)

Ronaldo Pornillosa Parreno Jr, Rowaldo Del Mundo

 

KEYWORDS

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

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.

 

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