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A Multi-dimension Clustering Method for Load Profiles of Australian Local Government Facilities

Azizivahed, A., Rupf, G.V., Lund, C., Arefi, A.ORCID: 0000-0001-9642-7639, Walia, J., Rahman, Md.M. and Islam, Md.R. (2021) A Multi-dimension Clustering Method for Load Profiles of Australian Local Government Facilities. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), 17 - 19 December 2021, Arad, Romania

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The clustering of historical electricity consumption data is an effective means of developing representative load profiles for long-term energy planning. This paper presents a multi-dimensional approach for clustering, considering scattering and separation metrics and the number of clusters. A novel hybrid approach to solve the clustering function is also proposed: a combination of Invasive Weed Optimization (IWO) and wavelet mutation strategy. The hybrid method is applied to half-hourly metered electricity consumption data from the Civic Centre of a large local (municipal) government in Perth, Western Australia, to create representative seasonal load profiles. The novel clustering approach is then tested against the well-known k-means method using Davies-Bouldin and silhouette indices. In each seasonal clustered profile, the hybrid method is found to outperform the k-means method. The hybrid method has been identified as an effective clustering approach for analyzing the behavior of loads and assisting the identification of suitable energy efficiency initiatives.

Item Type: Conference Paper
Murdoch Affiliation(s): Engineering and Energy
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