Comparison of methods of including stochastic factors into deterministic models of indoor air quality |
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| Affiliation: | 1. Institute of Future Cities, Chinese University of Hong Kong, Hong Kong S.A.R, PR China;2. School of Architecture, Chinese University of Hong Kong, Hong Kong S.A.R, PR China;3. Institute of Environment, Energy and Sustainability, Chinese University of Hong Kong, Hong Kong S.A.R, PR China;4. Department of Land Surveying and Geo-informatics, Hong Kong Polytechnic University, Hong Kong S.A.R., PR China;5. Center for Space and Remote Sensing Research, National Central University, Taiwan;6. Graduate Institute of Space Science, National Central University, Taiwan;7. Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong S.A.R., PR China;8. Department of Atmospheric Sciences, National Central University, Taiwan;9. Department of Atmospheric Science, Yonsei University, Seoul, Republic of Korea;1. College of Urban Construction, Nanjing Tech University, Nanjing, 210009, PR China;2. College of National Defense Engineering, Army Engineering University, Nanjing, 210007, PR China;3. Department of Building Science, Tsinghua University, Beijing, 100084, PR China;1. Department of Building Science, School of Architecture, Tsinghua University, Beijing, China;2. Beijing Key Lab of Indoor Air Quality Evaluation and Control, Beijing, 100084, China;3. Beijing Infrastructure Investment Company, Beijing, China;1. Dstl, Porton Down, Salisbury, Wiltshire, SP4 0JQ, United Kingdom;2. National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA;3. Naval Surface Warfare Center, Dahlgren, VA 22448-5100, USA;4. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA;5. Aeris, 1723 Madison Ct., Louisville, CO 80027, USA |
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| Abstract: | The paper discusses problems connected with the inclusion of stochastic factors in deterministic models of indoor air quality (IAQ). Three different methods are shortly presented: quasi-dynamic multizone modelling with generation of input data time series; multizone modelling based on the theory of stochastic differential equations: and Monte Carlo simulation with independent random generation of stochastic parameters. The described methods are compared using a computer simulation of carbon dioxide concentration in a simple two-compartment office. The comparison of simulation results shows that the way in which stochastic disturbances are included in the models does not have an important influence on mean value of predicted carbon dioxide concentration. At the same time, the analysis of standard deviations indicates that the method of disturbance generation and its later incorporation into the IAQ models have a great influence on the probability distribution of estimated concentrations. Finally, there is a discussion on the main advantages and disadvantages of each of the proposed methods. |
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