TY - GEN
T1 - Development of a model to calculate the economic implications of improving the indoor climate
AU - Jensen, Kasper Lynge
PY - 2009/4/1
Y1 - 2009/4/1
N2 - The present Ph.d.-thesis constitutes the summary of a three year project period during which a methodology to estimate the effects of the indoor environment on performance of office work and the consequences for total building economy of modifying the indoor environment was developed. During the past decades several laboratory and field studies have documented an effect of the indoor environment on performance, but so far no calculation methodology or tool has been developed in order to utilise this knowledge. In the present project two models based on Bayesian Network (BN) probability theory have been developed; one model estimating the effects of indoor temperature on mental performance and one model estimating the effects of air quality on mental performance. Combined with dynamic building simulations and dose-response relationships, the derived models were used to calculate the total building economy consequences of improving the indoor environment. The Bayesian Network introduces new possibilities to create practical tools to assess the effects of the indoor environment on performance. The method evaluates among others the inherent uncertainty that exist when dealing with human beings in the indoor environment. Office workers exposed to the same indoor environment conditions will in many cases wear different clothing, have different metabolic rates, experience micro environment differences etc. all factors that make it difficult to estimate the effects of the indoor environment on performance. The Bayesian Network uses a probabilistic approach by which a probability distribution can take this variation of the different indoor variables into account. The result from total building economy calculations indicated that depending on the indoor environmental change (improvement of temperature or air quality), location of building and design of building a difference in the pay back time was observed. In a modern building located in a temperate climate zone, improving the air quality seemed more cost-beneficial than investment in mechanical cooling. In a hot climate, investment in cooling resulted in short pay back periods. Still several challenges exist before a tool to assess performance can be used on a daily basis in the building design phase. But the results from the present Ph.d.-thesis establishes the framework for a performance calculation tool that with further development has the possibility to help improve indoor environment conditions to the benefit of office workers and employers.
AB - The present Ph.d.-thesis constitutes the summary of a three year project period during which a methodology to estimate the effects of the indoor environment on performance of office work and the consequences for total building economy of modifying the indoor environment was developed. During the past decades several laboratory and field studies have documented an effect of the indoor environment on performance, but so far no calculation methodology or tool has been developed in order to utilise this knowledge. In the present project two models based on Bayesian Network (BN) probability theory have been developed; one model estimating the effects of indoor temperature on mental performance and one model estimating the effects of air quality on mental performance. Combined with dynamic building simulations and dose-response relationships, the derived models were used to calculate the total building economy consequences of improving the indoor environment. The Bayesian Network introduces new possibilities to create practical tools to assess the effects of the indoor environment on performance. The method evaluates among others the inherent uncertainty that exist when dealing with human beings in the indoor environment. Office workers exposed to the same indoor environment conditions will in many cases wear different clothing, have different metabolic rates, experience micro environment differences etc. all factors that make it difficult to estimate the effects of the indoor environment on performance. The Bayesian Network uses a probabilistic approach by which a probability distribution can take this variation of the different indoor variables into account. The result from total building economy calculations indicated that depending on the indoor environmental change (improvement of temperature or air quality), location of building and design of building a difference in the pay back time was observed. In a modern building located in a temperate climate zone, improving the air quality seemed more cost-beneficial than investment in mechanical cooling. In a hot climate, investment in cooling resulted in short pay back periods. Still several challenges exist before a tool to assess performance can be used on a daily basis in the building design phase. But the results from the present Ph.d.-thesis establishes the framework for a performance calculation tool that with further development has the possibility to help improve indoor environment conditions to the benefit of office workers and employers.
M3 - PhD thesis
PB - Technical University of Denmark
ER -