Finding the different patterns in buildings data using bag of words
  representation with clustering
  The understanding of the buildings operation has become a challenging task
due to the large amount of data recorded in energy efficient buildings. Still,
today the experts use visual tools for analyzing the data. In order to make the
task realistic, a method has been proposed in this paper to automatically
detect the different patterns in buildings. The K Means clustering is used to
automatically identify the ON (operational) cycles of the chiller. In the next
step the ON cycles are transformed to symbolic representation by using Symbolic
Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag
of words representation for hierarchical clustering. Moreover, the proposed
technique is applied to real life data of adsorption chiller. Additionally, the
results from the proposed method and dynamic time warping (DTW) approach are
also discussed and compared.
