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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,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.
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.
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.