An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings

Li, K, Yang, J, Robinson, D, Ma, J and Ma, Z 2019, 'An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings', Energy, vol. 174, pp. 735-748.


Document type: Journal Article
Collection: Journal Articles

Title An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings
Author(s) Li, K
Yang, J
Robinson, D
Ma, J
Ma, Z
Year 2019
Journal name Energy
Volume number 174
Start page 735
End page 748
Total pages 14
Publisher Elsevier
Abstract This study presents an agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings. The proposed strategy takes the advantages of three dissimilarity measures (i.e. Euclidean distance, Pearson distance and Chebyshev distance) to calculate the difference between daily electricity usage profiles. Two-year hourly electricity usage data collected from two different university library buildings were employed to evaluate the performance of this strategy. It was shown that this strategy, which considered both magnitude dissimilarity and variation dissimilarity simultaneously, can identify more informative typical daily electricity usage profiles, in comparison with other twelve clustering-based strategies which used a single dissimilarity measure. Some interesting information related to building energy usage behaviours was also discovered with the help of visualisation techniques. Additional or hidden information discovered using this strategy can potentially be useful for fault detection and diagnosis and performance enhancement of library buildings.
Subject Building Construction Management and Project Planning
Building Science and Techniques
Keyword(s) Building electricity usage
Cluster analysis
Multiple dissimilarity measures
Shared Nearest Neighbours
DOI - identifier 10.1016/j.energy.2019.03.003
Copyright notice © 2019 Elsevier Ltd. All rights reserved.
ISSN 0360-5442
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 4 Abstract Views  -  Detailed Statistics
Created: Mon, 29 Apr 2019, 13:04:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us