Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

Muhammad Aftab, Chien Chen, Chi Kin Chau, Talal Rahwan

    Research output: Contribution to journalArticle

    Abstract

    Intelligent building automation systems can reduce the energy consumption of heating, ventilation and air-conditioning (HVAC) units by sensing the comfort requirements automatically and scheduling the HVAC operations dynamically. Traditional building automation systems rely on fairly inaccurate occupancy sensors and basic predictive control using oversimplified building thermal response models, all of which prevent such systems from reaching their full potential. Such limitations can now be avoided due to the recent developments in embedded system technologies, which provide viable low-cost computing platforms with powerful processors and sizeable memory storage in a small footprint. As a result, building automation systems can now efficiently execute highly sophisticated computational tasks, such as real-time video processing and accurate thermal-response simulations. With this in mind, we designed and implemented an occupancy-predictive HVAC control system in a low-cost yet powerful embedded system (using Raspberry Pi 3) to demonstrate the following key features for building automation: (1) real-time occupancy recognition using video-processing and machine-learning techniques, (2) dynamic analysis and prediction of occupancy patterns, and (3) model predictive control for HVAC operations guided by real-time building thermal response simulations (using an on-board EnergyPlus simulator). We deployed and evaluated our system for providing automatic HVAC control in the large public indoor space of a mosque, thereby achieving significant energy savings.

    Original languageEnglish (US)
    Pages (from-to)141-156
    Number of pages16
    JournalEnergy and Buildings
    Volume154
    DOIs
    StatePublished - Nov 1 2017

    Fingerprint

    Model predictive control
    Embedded systems
    Air conditioning
    Ventilation
    Heating
    Automation
    Costs
    Intelligent buildings
    Processing
    Dynamic analysis
    Learning systems
    Energy conservation
    Energy utilization
    Simulators
    Scheduling
    Control systems
    Data storage equipment
    Sensors
    Hot Temperature

    Keywords

    • Automatic HVAC control
    • Embedded system
    • Model predictive control
    • Occupancy recognition

    ASJC Scopus subject areas

    • Civil and Structural Engineering
    • Building and Construction
    • Mechanical Engineering
    • Electrical and Electronic Engineering

    Cite this

    Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system. / Aftab, Muhammad; Chen, Chien; Chau, Chi Kin; Rahwan, Talal.

    In: Energy and Buildings, Vol. 154, 01.11.2017, p. 141-156.

    Research output: Contribution to journalArticle

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