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

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 realtime 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 videoprocessing 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.

Post-occupancy evaluation: State-of-the-art analysis and state-of-the-practice review

“…Aftab et. al [145]. recently developed an occupancy-recognition algorithm to count the number of people crossing a virtual reference line (near the entrance) in the video captured by a fisheye camera.…”

Estimating the savings potential of occupancy-based heating strategies

“…It should be clear that the technologies for occupancy-based space heating presented in this paper can in principle also be used in occupancy-based cooling schemes (or HVAC control systems in general) to save energy and cost (Gluck et al 2017). Aftab et al (2017) recently proposed an occupancy-based HVAC control system to save energy when cooling mosques. Peng et al (2018) examined the use of an occupancy-prediction-based cooling system in an office building in Singapore, where cooling is necessary due to the tropical climate, and found out that 7% to 52% cooling energy could be saved depending on the type of the room.…”

Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

“…Machine learning has been reported highly beneficial in the control systems of heating, ventilation, and air conditioning (HVAC) mechanisms [7][8][9]. Artificial neural networks (ANN), decision trees (DT), adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) are among the most popular machine learning methods used in HVAC control systems [10][11][12][13][14][15].…”