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Clustering based on dynamic time warping to extract typical daily patterns from long-term operation data of a ground source heat pump system

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  • Zhang, Shuyang
  • Zhang, Lun
  • Zhang, Xiaosong

Abstract

While clustering has been commonly used to profile the building electricity consumption data, its application to HVAC system data is relatively less. Based on the operation data of a residential ground source heat pump (GSHP) system, a pre-processing procedure was set up, including acquisition, cleaning, missing-data fill-in, and standardization. Then hierarchical clustering was used, based on the dynamic time warping (DTW) distance calculation method. A new index, the sum of squares of errors based on DTW, was used to determine the best cluster number. The patterns were extracted based on clustering results.

Suggested Citation

  • Zhang, Shuyang & Zhang, Lun & Zhang, Xiaosong, 2022. "Clustering based on dynamic time warping to extract typical daily patterns from long-term operation data of a ground source heat pump system," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006703
    DOI: 10.1016/j.energy.2022.123767
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    References listed on IDEAS

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    1. Du, Han & Zhou, Xinlei & Nord, Natasa & Carden, Yale & Ma, Zhenjun, 2023. "A new data mining strategy for performance evaluation of a shared energy recovery system integrated with data centres and district heating networks," Energy, Elsevier, vol. 285(C).

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