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Title: A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks

Abstract

This paper presents a reliable, scalable, and transferable framework to predict occupancy in a building utilizing diverse, multi-modal information. We propose a new methodology for learning-driven occupancy detection built on the concepts of probabilistic graphical modeling and observable Markov chain modeling. To capture the relationship between multi-sensor data and occupancy, we propose this Occ-STPN framework that is flexible to support both multivariate and univariate formulations. While the multivariate Occ-STPN performs feature-level fusion of multiple predictors and occupancy time-series data, the univariate Occ-STPN involves decision fusion of occupancy predictions using individual predictors based on a mutual information weighted fusion scheme. We also propose a new metric to evaluate the performance of occupancy prediction algorithms. Two popular datasets are used to validate our approach and demonstrate that our framework is scalable in terms of the number of information sources (e.g., sensors) as well as it is possible to transfer trained models from one building to another without significant reduction in performance. Reliability of the algorithm is also tested by injecting noise into the datasets.

Authors:
 [1];  [1]; ORCiD logo [2];  [3];  [1]
  1. Iowa State University
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  3. University of Colorado
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Grid Modernization Laboratory Consortium
OSTI Identifier:
1569444
Report Number(s):
NREL/CP-5D00-75055
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2019 American Control Conference (ACC), 10-12 July 2019, Philadelphia, Pennsylvania
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; building occupancy; spatiotemporal; Occ-STPN framework

Citation Formats

Tan, Sin Yong, Saha, Homagni, Florita, Anthony R, Henze, Gregor P., and Sarkar, Soumik. A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks. United States: N. p., 2019. Web.
Tan, Sin Yong, Saha, Homagni, Florita, Anthony R, Henze, Gregor P., & Sarkar, Soumik. A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks. United States.
Tan, Sin Yong, Saha, Homagni, Florita, Anthony R, Henze, Gregor P., and Sarkar, Soumik. Thu . "A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks". United States.
@article{osti_1569444,
title = {A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks},
author = {Tan, Sin Yong and Saha, Homagni and Florita, Anthony R and Henze, Gregor P. and Sarkar, Soumik},
abstractNote = {This paper presents a reliable, scalable, and transferable framework to predict occupancy in a building utilizing diverse, multi-modal information. We propose a new methodology for learning-driven occupancy detection built on the concepts of probabilistic graphical modeling and observable Markov chain modeling. To capture the relationship between multi-sensor data and occupancy, we propose this Occ-STPN framework that is flexible to support both multivariate and univariate formulations. While the multivariate Occ-STPN performs feature-level fusion of multiple predictors and occupancy time-series data, the univariate Occ-STPN involves decision fusion of occupancy predictions using individual predictors based on a mutual information weighted fusion scheme. We also propose a new metric to evaluate the performance of occupancy prediction algorithms. Two popular datasets are used to validate our approach and demonstrate that our framework is scalable in terms of the number of information sources (e.g., sensors) as well as it is possible to transfer trained models from one building to another without significant reduction in performance. Reliability of the algorithm is also tested by injecting noise into the datasets.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}

Conference:
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