• Abayomi Olawumi Federal University of Technology Akure
  • F.M. Dahunsi Federal University of Technology Akure




data mining, clustering, machine learning, consumption data


Data mining is a promising tool used in processing energy data collected from energy consumers. The knowledge derived from energy data is very pertinent in the formulation of various demand-side management programs. This paper uses clustering techniques to segment the energy consumption patterns of residential and commercial buildings; situated at different geographical locations. The two (2) commonly used clustering techniques: K-Means and Agglomerative Hierarchical Clustering, were employed. The result indicates that the choice of clustering technique for load profiling is highly subjective to the nature of the dataset. Hence, using Davies-Bouldin Index (DB) Index and Silhouette Index (SI) as clustering indicators to select an optimum number of clusters and the best clustering technique. Hierarchical clustering was identified as the most appropriate clustering for the two buildings.

Author Biographies

  • Abayomi Olawumi, Federal University of Technology Akure

    Abayomi Olawumi is a research assistant at the Smart Energy Research Laboratory at the Federal University of Technology Akure, he is simultaneously taking a Masters Programme in communication engineering at the departent of Electrical and Electronics Engineering at the same citadel of knowledge.

  • F.M. Dahunsi, Federal University of Technology Akure

    Dr.(Mrs.) Folasade Dahunsi is an associate professor in computer engineering at the Federal University of Technology,Akure, with major research interest in communication networks and system.


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Computer, Telecommunications, Software, Electrical & Electronics Engineering

How to Cite

LOAD PROFILING OF COMMERCIAL AND RESIDENTIAL BUILDING USING CLUSTERING TECHNIQUE. (2023). Nigerian Journal of Technology, 42(2), 257-263. https://doi.org/10.4314/njt.v42i2.14