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iLTC: Achieving Individual Comfort in Shared Spaces

Published: 15 February 2016 Publication History

Abstract

Automatic control of HVAC and artificial lights has been one of the popular methods for achieving energy-efficient buildings. The current systems operate using fixed set-point controls, which are usually based on a conservative approach. Additionally, the lighting systems require additional sensor deployment to cope up with continuous fluctuation of natural light intensity. In this work, we describe a smart system called indoor Lighting and Temperature Controller (iLTC), which eliminates the fixed set-points and requirement of additional light sensors. iLTC decides operating set-points more aggressively, which is energy optimal and it tries to provide maximal user comfort to all the co-occupants in a shared space. The flexibility in choosing energy optimal set-points stems from the knowledge of comprehensive temperature and lighting comfort functions of individuals. To track the fluctuations in natural light intensity, we employ a smart estimation technique that requires light measurements only once during the training phase. Using the proposed system, we show the energy consumption by HVAC can be reduced up to 39\%. Similarly, compared to traditional on/off based and multilevel lighting systems with iLTC, energy consumption can be reduced up to 33\% and 60\%, respectively. We also provide qualitative user experience evaluation.

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    Published In

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    EWSN '16: Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks
    February 2016
    366 pages
    ISBN:9780994988607

    Sponsors

    • EWSN: International Conference on Embedded Wireless Systems and Networks

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    Junction Publishing

    United States

    Publication History

    Published: 15 February 2016

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    Author Tags

    1. comfort preference learning
    2. individual comfort
    3. shared HVAC control
    4. shared lighting control
    5. shared space

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    EWSN '16
    Sponsor:
    • EWSN
    February 15 - 17, 2016
    Graz, Austria

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    Overall Acceptance Rate 81 of 195 submissions, 42%

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    • (2019)Machine Learning for Smart Building ApplicationsACM Computing Surveys10.1145/331195052:2(1-36)Online publication date: 27-Mar-2019
    • (2018)Where is PELE?ACM SIGBED Review10.1145/3231535.323153615:2(8-15)Online publication date: 5-Jun-2018
    • (2017)An energy-harvesting facade optimization system for built environmentsProceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/3137133.3141442(1-2)Online publication date: 8-Nov-2017
    • (2017)BuildsenseProceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/3137133.3137141(1-10)Online publication date: 8-Nov-2017
    • (2016)Robust sensor data collection over a long period using virtual sensingProceedings of the Workshop on Time Series Analytics and Applications10.1145/3014340.3014341(2-7)Online publication date: 6-Dec-2016

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