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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