Automatic HVAC Control with Real-time Occupancy Recognition and Simulation-guided Model Predictive Control in Low-cost Embedded System

By Muhammad Aftab, Chien Chen, Chi-Kin Chau, Talal Rahwan. 2017

171 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “In [94], Aftab et al. have combined four different parameters to predict building occupancy to control HVAC using low-cost embedded systems.”
Tackling Climate Change with Machine Learning (2019, methodology): “[361] Muhammad Aftab, Chien Chen, Chi-Kin Chau, and Talal Rahwan.”
Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence (2018, background): “Other examples of such studies in the context of HVAC can be found in [12] and [13].”