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Knowledge Discovery Using Topological Analysis for Building Sensor Data.

Manik Gupta1, Nigel Phillips2

  • 1Department of Computer Science and Information Systems, BITS-Pilani, Hyderabad 500078, India.

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|September 4, 2020
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Summary

This study uses Q-Analysis to analyze sensor data from smart buildings, revealing distinct floor behaviors independent of time or season. This method helps understand energy consumption in older buildings with Building Energy Management Systems (BEMS).

Keywords:
BEMSHVACInternet of ThingsQ-Analysiscomputational topologydata miningknowledge discovery

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Area of Science:

  • Building Science
  • Data Analysis
  • Complex Systems

Background:

  • Smart buildings utilize distributed sensor networks for energy consumption insights.
  • Retrofitting older buildings with Building Energy Management Systems (BEMS) presents challenges in analyzing large sensor data streams.
  • Extracting meaningful knowledge from complex building data requires advanced analytical methods.

Purpose of the Study:

  • To apply Q-Analysis, a topological approach, for summarizing large sensor data sets in smart buildings.
  • To reveal useful relationships between variables in Building Energy Management Systems (BEMS) data.
  • To identify macro-level building behaviors not apparent with traditional methods.

Main Methods:

  • Utilized Q-Analysis, a computationally simple topological method.
  • Extracted novel structural features termed Q-vectors.
  • Visualized Q-vector magnitudes to identify building floor behaviors.

Main Results:

  • Q-Analysis effectively summarized large sensor data sets.
  • Q-vector magnitude visualizations provided insights into macro building behaviors (e.g., floor-level activity).
  • Building floors demonstrated distinct behaviors correlated with set-point distribution, irrespective of time or season.

Conclusions:

  • Q-Analysis is a valuable tool for understanding complex building energy consumption patterns.
  • The method offers insights into building floor behaviors, complementing unsupervised learning on terminal units.
  • Building floor behavior is primarily influenced by set-point distribution rather than temporal factors.