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This paper proposes a method to support analysis of energy systems and validates it using operational data from a cold water chiller. The method automatically detects various operation patterns in the energy system.<\/jats:p>\n              <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>The use of k-means clustering is being proposed to automatically identify the On (operational) cycles of a system operating with a duty cycle. The latter\u2019s data is subsequently transformed to symbolic representations by using the symbolic aggregate approximation method. Afterward, the symbols are converted to bag of words representation (BoWR) for hierarchical clustering. A gap statistics method is used to find the best number of clusters in the data. Finally, operation patterns of the energy system are grouped together in each cluster. An adsorption chiller, operating under real life conditions, supplies the reference data for validation.<\/jats:p>\n              <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The proposed method has been compared with dynamic time warping (DTW) method using cophenetic coefficients and it has been shown that the BoWR has produced better results as compared to DTW. The results of BoWR are further investigated and for finding the optimal number of clusters, gap statistics have been used. At the end, interesting patterns of each cluster are discussed in detail.<\/jats:p>\n              <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The main goal of this research work is to provide analysis algorithms that automatically find the various patterns in the energy system of a building using as little configuration or field knowledge as possible. 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