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A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings

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  • Ma, Zhenjun
  • Yan, Rui
  • Nord, Natasa

Abstract

This paper presents a variation focused cluster analysis strategy to identify typical daily heating energy usage profiles of higher education buildings. Different from the existing cluster analysis studies which were primarily developed using Euclidean distance as the dissimilarity measure and tended to group the daily load profiles with similar magnitudes, Partitioning Around Medoids (PAM) clustering algorithm with Pearson Correlation Coefficient-based dissimilarity measure was used in this study to group the daily load profiles on the basis of the variation similarity. A comparison of the proposed strategy with a k-means cluster analysis with Euclidean distance as the dissimilarity measure was also performed. The performance of the proposed strategy was tested and evaluated using the three-year hourly heating energy usage data collected from 19 higher education buildings in Norway. The results demonstrated the effectiveness of the proposed strategy in identifying the typical daily energy usage profiles. The identified typical heating load profiles provided the information such as the peaks and troughs of the daily heating demand, daily high heating demand period and daily load variation. The identified profiles also helped to categorize multiple buildings into different groups in terms of the similar energy usage behaviors to support further energy efficiency initiatives.

Suggested Citation

  • Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
    • Handle: RePEc:eee:energy:v:134:y:2017:i:c:p:90-102
    DOI: 10.1016/j.energy.2017.05.191
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    1. Christoph Flath & David Nicolay & Tobias Conte & Clemens Dinther & Lilia Filipova-Neumann, 2012. "Cluster Analysis of Smart Metering Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(1), pages 31-39, February.
    2. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    3. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    4. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    5. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter, 1997. "Clustering in an Object-Oriented Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 1(i04).
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    14. Anders Rhiger Hansen & Daniel Leiria & Hicham Johra & Anna Marszal-Pomianowska, 2022. "Who Produces the Peaks? Household Variation in Peak Energy Demand for Space Heating and Domestic Hot Water," Energies, MDPI, vol. 15(24), pages 1-23, December.
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