Finding the different patterns in buildings data using bag of words representation with clustering

TL;DR

This paper presents a novel method combining clustering, SAX, and bag of words to automatically identify operational patterns in building energy data, demonstrated on real chiller data and compared with DTW.

Contribution

It introduces an automated approach using clustering and symbolic representation for pattern detection in building energy data, enhancing analysis without visual tools.

Findings

Effective detection of chiller ON cycles

Comparable performance with DTW approach

Applicable to real-world building data

Abstract

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.