Journal
APPLIED ENERGY
Volume 194, Issue -, Pages 333-342Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2016.06.046
Keywords
Cluster analysis; k-means; Smart meter measurements; Load estimation
Categories
Funding
- EPSRC Increasing the Observability of Electrical Distribution Systems using Smart Meters (IOSM) Project [EP/J00944X/1]
- P2P-SmarTest Programme through the European Commission HORIZON grant [H2020-646469]
- UK-China NSFC/EPSRC OPEN Project [EP/K006274/1, 51261130473]
- EPSRC [EP/J00944X/1, EP/E036503/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/J00944X/1, EP/E036503/1] Funding Source: researchfish
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A load estimation algorithm based on k-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. (C) 2016 The Authors. Published by Elsevier Ltd.
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