Study of Adaptive Model Predictive Control for Cyber-Physical Home Systems

Ooi, Sian En; Yuan, Fang; Lim, Yuto; Tan, Yasuo

doi:10.1007/978-981-13-2622-6_17

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Nguyen et al used the House Thermal Simulator developed in [3] to propose a simple model predictive controller (MPC) in [6] to control the heating, ventilation, and air conditioning (HVAC) systems in residential houses through simulation and optimised the controller for both energy efficiency and thermal comfort. Next, Ooi et al developed their mathematical models in [7] for developing and optimising a conventional MPC via computer simulation to maintain the air temperature of one of the bedrooms in iHousethis work is then improved by Ooi et al in [8] by using the adaptive MPC that utilises two types of online model estimation system, the Kalman filter (KF) state estimator and the linear time-varying Kalman filter (LTVKF) estimator for increasing the accuracy of the controller"s internal plant model.…”

Section: Introductionmentioning

confidence: 99%

Black box modelling and simulating the dynamic indoor air temperature of a laboratory using autoregressive–moving-average (ARMA) model

Hussein

Sharifmuddin

rabeei

et al. 2021

IJEECS

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<p>Mathematical model representing the dynamic indoor air temperature of a building is important for reducing the time and cost required to test any proposed thermal comfort control algorithm and strategy for that building through computer simulation. There are many types of mathematical model, and each type has its strength(s) and weakness(es). An autoregressive–moving-average (ARMA) model, a type of black box model is used to represent the dynamic indoor air temperature behaviour of industrial instrumentation laboratory at malaysia-japan international institute of technology (MJIIT), universiti teknologi malaysia (UTM) Kuala Lumpur based on the recorded data from the laboratory and minimal physical characteristics knowledge of the laboratory. The ARMA model’s output developed in this research is compared with the actual data recorded from the laboratory for performance measurement. The obtained result shows that the ARMA model is sufficient for modelling and simulating the dynamic indoor air temperature behaviour of the laboratory.</p>

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Section: Introductionmentioning

confidence: 99%

Black box modelling and simulating the dynamic indoor air temperature of a laboratory using autoregressive–moving-average (ARMA) model

Hussein

Sharifmuddin

rabeei

et al. 2021

IJEECS

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“…In other words, the human has a strong interaction with all the smart home systems and their surrounding environments. Unlike the previous works on smart homes and human interaction, Ooi et al [3] present an adaptive model predictive control (MPC) based controller that is integrated into the existing EETCC system for the CPHS environment. One of the significant this work is that the adaptive MPC based controller can monitor the temperature in a real-time manner by using the sensed raw environmental data from the experimental house, iHouse.…”

Section: Introductionmentioning

confidence: 99%

Study of Human Thermal Comfort for Cyber–Physical Human Centric System in Smart Homes

Fang

Lim

Ooi

et al. 2020

Sensors

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An environmental thermal comfort model has previously been quantified based on the predicted mean vote (PMV) and the physical sensors parameters, such as temperature, relative humidity, and air speed in the indoor environment. However, first, the relationship between environmental factors and physiology parameters of the model is not well investigated in the smart home domain. Second, the model that is not mainly for an individual human model leads to the failure of the thermal comfort system to fulfill the human’s comfort preference. In this paper, a cyber–physical human centric system (CPHCS) framework is proposed to take advantage of individual human thermal comfort to improve the human’s thermal comfort level while optimizing the energy consumption at the same time. Besides that, the physiology parameter from the heart rate is well-studied, and its correlation with the environmental factors, i.e., PMV, air speed, temperature, and relative humidity are deeply investigated to reveal the human thermal comfort level of the existing energy efficient thermal comfort control (EETCC) system in the smart home environment. Experimental results reveal that there is a tight correlation between the environmental factors and the physiology parameter (i.e., heart rate) in the aspect of system operational and human perception. Furthermore, this paper also concludes that the current EETCC system is unable to provide the precise need for thermal comfort to the human’s preference.

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Black Box Modelling and Simulating the Dynamic Indoor Air Temperature of a Laboratory Using the Continuous-Time Transfer Function Model

Hussein¹,

Sharifmuddin²,

Kasai³

et al. 2020

Proceedings of the Third International Conference on Separation Technology 2020 (ICoST 2020)

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Study of Adaptive Model Predictive Control for Cyber-Physical Home Systems

Cited by 3 publications

References 5 publications

Black box modelling and simulating the dynamic indoor air temperature of a laboratory using autoregressive–moving-average (ARMA) model

Black box modelling and simulating the dynamic indoor air temperature of a laboratory using autoregressive–moving-average (ARMA) model

Study of Human Thermal Comfort for Cyber–Physical Human Centric System in Smart Homes

Black Box Modelling and Simulating the Dynamic Indoor Air Temperature of a Laboratory Using the Continuous-Time Transfer Function Model

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