Abstract

Modern buildings consist of hundreds of sensors and actuators for monitoring and operation of systems such as HVAC, light and security. To enable portable applications in next generation smart buildings, we need models and standardized ontologies that represent these sensors across diverse types of buildings. Recent research has shown that extracting information such as sensor type with available metadata and timeseries data analysis is difficult due to heterogeneity of systems and lack of support for interoperability. We propose perturbations in the control system as a mechanism to increase the observability of building systems to extract contextual information and develop standardized models. We design Quiver, an experimental framework for actuation of building HVAC system that enables us to perturb the control system safely. Using Quiver, we demonstrate three applications using empirical experiments on a real commercial building: colocation of data points, identification of point type and mapping of dependency between actuators. Our results show that we can colocate data points in HVAC terminal units with 98.4 % accuracy and 63 % coverage. We can identify point types of the terminal units with 85.3 % accuracy. Finally, we map the dependency links between actuators with an accuracy of 73.5 %, with 8.1 % and 18.4 % false positives and false negatives respectively.

Bibtex

@article{DBLP:journals/corr/KohBAAG16,
  author    = {Jason Koh and
               Bharathan Balaji and
               Vahideh Akhlaghi and
               Yuvraj Agarwal and
               Rajesh Gupta},
  title     = {Quiver: Using Control Perturbations to Increase the Observability
               of Sensor Data in Smart Buildings},
  journal   = {CoRR},
  volume    = {abs/1601.07260},
  year      = {2016},
  url       = {http://arxiv.org/abs/1601.07260},
  archivePrefix = {arXiv},
  eprint    = {1601.07260},
  timestamp = {Mon, 13 Aug 2018 16:46:34 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/KohBAAG16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Plain Text

Koh, Jason, et al. "Quiver: Using control perturbations to increase the observability of sensor data in smart buildings." arXiv preprint arXiv:1601.07260 (2016).