Smart Sensing for HVAC Control: Collaborative Intelligence in Optical and IR Cameras

Cao, Ningyuan; Ting, Justin; Sen, Shreyas; Raychowdhury, Arijit

doi:10.1109/tie.2018.2818665

Cited by 48 publications

(43 citation statements)

References 23 publications

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“…In addition to the above mentioned, many other cases have been studied on HVAC [18][19][20][21][22][23][24][25][26][27]. In the present paper, a model predictive control (MPC) method has been used for controlling temperature and humidity which was previously only introduced for controlling temperature in [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

et al. 2019

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Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy.

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Section: Related Workmentioning

confidence: 99%

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

et al. 2019

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“…Wu et al [30] have used a motion sensing approach for occupancy sensing where machine learning has been used. Study towards similar approaches of smart sensing is also carried out by Cao et al [31] with focus on intelligence building process with primary concern about energy management. Santra et al [32] have carried out occupancy sensing using continuous waves of frequency modulation for effective sensing capability.…”

Section: A the Backgroundmentioning

confidence: 99%

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

K.Mane*,

Rao

2019

IJITEE

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Occupancy sensing is one of predominant technology used in various control and context aware systems. The efficiency of such systems is highly corelated with the level of accuracy of the classification model of activity or stage detection of the subject or an occupant. The classification approaches based on computer need to evolve optimally for balancing the computational and time complexity with the classication accuracy using occupancy data acquired from Doppler radar. The proposed study presents a simplified framework that emphasizes on feature extraction technique as a medium to obtain more precision in the process of monitoring occupancy. The proposed logic has been implemented using analytical methodology convention and the extracted feature has been subjected to different forms of frequently used machine learning process with respect to processing time inclusive of training and testing period, efficiency, and accuracy of the proposed system.

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“…Sometimes, it is enough to know whether a zone has been occupied or not. However, in many cases, such as high-level energy saving via demand-controlled heating, ventilation, and air conditioning (HVAC) systems, it is required to know the number of occupants in different zones and its variation during the day [1][2][3]. There exist many works in the occupancy detection of the building zones using different sensors such as passive infrared (PIR), carbon dioxide (CO 2 ) concentration, temperature, humidity, and light beam whose main goal is to figure out whether an area of interest (AoI) is occupied or not [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array

Rabiee

Karlsson

2021

Remote Sensing

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Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor’s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor’s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node’s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset.

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Smart Sensing for HVAC Control: Collaborative Intelligence in Optical and IR Cameras

Cited by 48 publications

References 23 publications

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array

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