Abstract
Demand controlled ventilation is an energy saving approach used to regulate outdoor air supply to a space according to its demand. The occupancy within a space is a useful parameter reflecting the ventilation requirement. The objective of the present study is to develop a method for estimating the occupancy in a subway station based on CO2 and PM10 concentration data using the Indoor Air Quality Tele-Monitoring System located in the station. A feasibility study has been conducted to investigate the monitoring system can provide occupancy information with satisfactory accuracy for ventilation control purposes. Bayesian inference is used in estimating the occupancy at a platform based on unknown information such as ventilation rate and CO2 generation rate per person using various assumptions. The posterior distribution of the occupancy was simulated using the Markov Chain Monte Carlo sampling method. The results indicate that the dynamic model reduces the effect of the time delay and improves the uncertainty bands in the occupancy inference more than the static model. The inferred occupancy results are within the uncertainty ranges of the actual occupancy in the station. Additional use of the PM10 concentration data improves the accuracy of the inference further.
Highlights
The results indicate that the dynamic model reduces the effect of the time delay and improves the uncertainty bands in the occupancy inference more than the static model
The building energy used for HVAC is about 49% of the total energy consumption of commercial buildings in Korea[1].There have been many studies on energy saving efforts in buildings including the development of efficient systems and renewable energy sources as well as the implementation of smart system operation strategies
Demand-controlled ventilation (DCV) is an energy saving approach for regulating the amount of outdoor air supplied to a building based on demand
Summary
The building energy used for HVAC is about 49% of the total energy consumption of commercial buildings in Korea[1].There have been many studies on energy saving efforts in buildings including the development of efficient systems and renewable energy sources as well as the implementation of smart system operation strategies. Air-to-air heat exchangers have been used to recover exhaust energy [2,3,4] and various ventilation control strategies have been proposed for HVAC systems [5,6,7,8,9,10]. Demand-controlled ventilation (DCV) is an energy saving approach for regulating the amount of outdoor air supplied to a building based on demand. It is important to choose appropriate control parameters reflecting the demand for ventilation adequately. The control parameters include the concentrations of various indoor contaminants and the strengths of contaminant sources. It is difficult to reflect the room demand for ventilation based on a single control parameter out of many contaminant concentrations.
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