Occupancy Estimation Based on Indoor CO ₂ Concentration: Comparison of Neural Network and Bayesian Methods

다국어 초록 (Multilingual Abstract)

<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>