Propagating sensor uncertainty to better infer office occupancy in smart building control

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
66 Downloads (Pure)

Abstract

Occupant presence and behaviour in buildings is considered a key element towards building intelligent and pervasive environments. Yet, practical applications of energy intelligent buildings typically suffer from high sensor unreliability. In this work, we propose a layered probabilistic framework for occupancy-based control in intelligent buildings. We adopt a cascade of layers, where each layer addresses different aspects of the occupancy detection problem in a probabilistic manner rather than in a hard rule engine. We show that propagating uncertainty through each layer instead of standard hard decision outcomes improves the overall system performance. This finding suggests that smart building interfaces and communication data formats may need to input and output probabilistic data rather than simple discrete classification outputs. System performance and user comfort were evaluated with real life radar sensor data, based on an algorithm that allows real-time (casual) processing. Energy savings of up to 30% were obtained, compared to baseline measurements, while maintaining user comfort.

Original languageEnglish
Pages (from-to)73-82
Number of pages10
JournalEnergy and Buildings
Volume179
DOIs
Publication statusPublished - 15 Nov 2018

Keywords

  • Energy management
  • HMM
  • Occupancy detection
  • Radar sensors
  • Soft decision

Fingerprint Dive into the research topics of 'Propagating sensor uncertainty to better infer office occupancy in smart building control'. Together they form a unique fingerprint.

Cite this