k-means based load estimation of domestic smart meter measurements

Al-Wakeel, Ali ORCID: https://orcid.org/0000-0003-4970-7309, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Jenkins, Nicholas ORCID: https://orcid.org/0000-0003-3082-6260 2017. k-means based load estimation of domestic smart meter measurements. Applied Energy 194 , pp. 333-342. 10.1016/j.apenergy.2016.06.046

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Abstract

A load estimation algorithm based on kk-means cluster analysis was developed. The algorithm applies cluster centres – of previously clustered load profiles – and distance functions to estimate missing and future measurements. Canberra, Manhattan, Euclidean, and Pearson correlation distances were investigated. Several case studies were implemented using daily and segmented load profiles of aggregated smart meters. Segmented profiles cover a time window that is less than or equal to 24 h. Simulation results show that Canberra distance outperforms the other distance functions. Results also show that the segmented cluster centres produce more accurate load estimates than daily cluster centres. Higher accuracy estimates were obtained with cluster centres in the range of 16–24 h. The developed load estimation algorithm can be integrated with state estimation or other network operational tools to enable better monitoring and control of distribution networks.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Funders:	EPSRC, European Commission Horizon 2020, National Natural Science Foundation of China
Date of First Compliant Deposit:	27 June 2016
Date of Acceptance:	12 June 2016
Last Modified:	02 May 2023 14:51
URI:	https://orca.cardiff.ac.uk/id/eprint/92137

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