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Haolia Rahman,Hwang Hojong(황호종),Bong Seong Hyeok(봉성혁),Kyung-Jin Jang(장경진),Hwataik Han(한화택) 대한기계학회 2016 대한기계학회 춘추학술대회 Vol.2016 No.12
The accuracy of reading sensor depends on the operation range and the reliability level of sensor; therefore, uncertainty cannot be avoided. This study is proposed to assess the uncertainty of a CO₂ reading sensor during estimating the occupancy distribution in office multi-room buildings. Bayesian Markov Chain Monte Carlo approach used to calculate the occupancy in the individual space based on CO₂ information. CONTAM was used to generate CO₂ concentration from given information of occupancy schedule, building model and ventilation rate in each room. Noise levels were added into CO₂ data to investigate an acceptable uncertainty in the occupancy estimation.
Comparison of numerical algorithms to estimate number of occupants based on CO₂ concentration
Haolia Rahman,Hwataik Han(한화택) 대한설비공학회 2016 대한설비공학회 학술발표대회논문집 Vol.2016 No.11
Two algorithms to estimate number of occupants in a room is presented in this study. Neural network and Bayesian Markov chain Monte Carlo are applied to solve several cases drawn from occupancy estimation. NN is used to train an input data set of CO₂ concentration, airflow and occupancy patterns with tapped delay lines. The second algorithm is Bayesian MCMC which relies on statistical approach of given CO₂ data. The estimation errors and characteristics of the two methods are compared in estimating number of occupants using carbon dioxide concentration in a room.
Haolia Rahman,Hwataik Han 대한설비공학회 2017 International Journal of Air-Conditioning and Refr Vol.25 No.3
The number of occupants in a space can significantly affect ventilation control. Using neural network and Bayesian Markov chain Monte Carlo (MCMC) methods, this study estimates the number of occupants based on CO2 concentration in a room. The abilities of both methods to recognize the input-parameter characteristics are compared under certain circumstances, and the parameters are optimized to improve the estimation accuracy. The neural network trains an input dataset of CO2 concentrations, ventilation rates, and occupancy patterns with tapped delay lines. Meanwhile, the Bayesian MCMC calculates the given CO2 data by a mathematical model based on a statistical approach. The present space model is a single-office room in which the CO2 concentration is determined through several simulation schemes and experiments. The estimation accuracy of the neural network depends on the complexity of the input parameters (i.e., CO2 concentration and ventilation rate), whereas the Bayesian MCMC is influenced by uncertainty in the CO2 concentration. Both methods produce acceptable estimates under certain treatments.
Rahman, Haolia,Han, Hwataik World Scientific Publishing Company 2017 International Journal of Air-Conditioning and Refr Vol.25 No.3
<P>The number of occupants in a space can significantly affect ventilation control. Using neural network and Bayesian Markov chain Monte Carlo (MCMC) methods, this study estimates the number of occupants based on CO<SUB>2</SUB> concentration in a room. The abilities of both methods to recognize the input-parameter characteristics are compared under certain circumstances, and the parameters are optimized to improve the estimation accuracy. The neural network trains an input dataset of CO<SUB>2</SUB> concentrations, ventilation rates, and occupancy patterns with tapped delay lines. Meanwhile, the Bayesian MCMC calculates the given CO<SUB>2</SUB> data by a mathematical model based on a statistical approach. The present space model is a single-office room in which the CO<SUB>2</SUB> concentration is determined through several simulation schemes and experiments. The estimation accuracy of the neural network depends on the complexity of the input parameters (i.e., CO<SUB>2</SUB> concentration and ventilation rate), whereas the Bayesian MCMC is influenced by uncertainty in the CO<SUB>2</SUB> concentration. Both methods produce acceptable estimates under certain treatments.</P>
Haolia Rahman,Hwataik Han 대한설비공학회 2017 대한설비공학회 학술발표대회논문집 Vol.2017 No.6
We introduce Bayesian MCMC approach for occupancy estimation in a room with immeasurable ventilation rate. Measured CO2 concentration is used as an input of Bayesian calculation and a parameter for quantifying the ventilation rate. The quantification of ventilation rate is obtained from concentration decay method. CO2 concentration decay rates were determined from the sample data at nights with no occupancy and averaged to obtain the steady state ventilation rate. The Bayesian calculation uses a mathematical model based on dynamic mass balance equation in the space. The result shows that the accuracy of occupancy estimation depends on the ventilation rate estimated as well as the uncertainty in CO2 measurements.
Recognition of local occupancy variation based on CO₂ concentration using Bayesian MCMC method
Haolia Rahman,Hwataik Han 대한설비공학회 2016 대한설비공학회 학술발표대회논문집 Vol.2016 No.6
Demand controlled ventilation is a control strategy for energy-efficient ventilation by regulating outdoor air supply into the space based on the demand. Number of occupants represents the ventilation demand in most office spaces to dilute indoor contaminants. This study is proposed to develop a method to estimate the occupancy distributions in buildings based on CO₂ indoor concentrations using Bayesian method. The Markov Chain Monte Carlo approach is applied to a hospital building based on the simulated CO₂ concentration data by arbitrary occupant schedules and distributions in the rooms. Various system parameter of MCMC are assessed. The results show how well information from previous data and parameter can improve the prediction.
Real-time control of ventilation rate based on Bayesian estimation of occupants
Haolia Rahman,Hwataik Han 대한설비공학회 2018 대한설비공학회 학술발표대회논문집 Vol.2018 No.6
It would be a smart way of maintaining indoor air quality while minimizing HVAC energy consumption to supply fresh outdoor air according to the demand i.e. proportional to the number of occupants in office buildings. In this paper, the supply airflow rate is controlled according to the estimated real-time occupancy in a small office building. Bayesian MCMC method has been adopted in order to estimate the occupancy based on instantaneous indoor CO₂ concentration and current ventilation rate. A scheduled ventilation control scheme is compared with the occupancy based ventilation control scheme. The occupancy control scheme has been found to be successful on a real-time basis with a reasonable delay. The average of ventilation rate per person using the occupancy control scheme was close to ASHRAE standard reference and much smaller compared to the scheduled control scheme.
Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation
Azimil Gani Alam,Haolia Rahman,김중경,한화택 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.5
Demand control ventilation is employed to save energy by adjusting airflow rate according to the ventilation load of a building. This paper investigates a method for occupancy estimation by using a dynamic neural network model based on carbon dioxide concentration in an occupied zone. The method can be applied to most commercial and residential buildings where human effluents to be ventilated. An indoor simulation program CONTAMW is used to generate indoor CO 2 data corresponding to various occupancy schedules and airflow patterns to train neural network models. Coefficients of variation are obtained depending on the complexities of the physical parameters as well as the system parameters of neural networks, such as the numbers of hidden neurons and tapped delay lines. We intend to identify the uncertainties caused by the model parameters themselves, by excluding uncertainties in input data inherent in measurement. Our results show estimation accuracy is highly influenced by the frequency of occupancy variation but not significantly influenced by fluctuation in the airflow rate. Furthermore, we discuss the applicability and validity of the present method based on passive environmental conditions for estimating occupancy in a room from the viewpoint of demand control ventilation applications.