A Multi-dimension Clustering Method for Load Profiles of Australian Local Government Facilities
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2021 IEEE 6th International Conference on Computing, Communication and Automation, ICCCA 2021, 2022, 00, pp. 819-825
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| A_Multi-dimension_Clustering_Method_for_Load_Profiles_of_Australian_Local_Government_Facilities.pdf | Published version | 1.75 MB |
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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.
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