Finding the different patterns in buildings data using bag of words representation with clustering
This work addresses the problem of inefficient data analysis for energy-efficient building experts, offering an automated alternative to visual tools, though it is incremental as it combines existing techniques like clustering and SAX.
The paper tackled the challenge of analyzing large building operation data by proposing a method to automatically detect different patterns in buildings, specifically identifying operational cycles of a chiller using K-Means clustering and symbolic representation, and applied it to real adsorption chiller data with comparisons to dynamic time warping.
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.