A Multi-dimension Clustering Method for Load Profiles of Australian Local Government Facilities
2021 IEEE 6th International Conference on Computing, Communication and Automation, ICCCA 2021
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.
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