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

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.The heat exchange on the user-side during one cooling season was processed as a case study. We obtained 5 valid clusters and extracted patterns from each. For the studied case, the result based on the DTW method yields a better homogeneity compared to the Euclidean method. The method is then applied to a five-year operation data, where 9 and 6 patterns were obtained for the cooling and heating seasons, respectively. They mainly differed in shape, and the cooling patterns fluctuate more. Overall, clustering and pattern extraction can reduce the data dimension, and provide a data-driven view of how the system supplies the cooling and heating.