A Bayesian Network approach has been developed that can compare different building designs by estimating the effects of the thermal indoor environment on the mental performance of office workers. A part of this network is based on the compilation of subjective thermal sensation data and the associated objective thermal measurements from 12,000 office occupants from different parts of the world. A Performance Index (Π) is introduced that can be used to compare directly the different building designs and furthermore to assess the total economic consequences of the indoor climate with a specific building design. In this paper, focus will be on the effects of temperature on mental performance and not on other indoor climate factors. A total economic comparison of six different building designs, four located in northern Europe and two in Los Angeles, USA, was performed. The results indicate that investments in improved indoor thermal conditions can be justified economically in most cases. The Bayesian Network provides a reliable platform using probabilities for modelling the complexity while estimating the effect of indoor climate factors on human beings, due to the different ways in which humans are affected by the indoor climate.
Until now, it has been problematic to integrate the effects of indoor climate on office workers’ performance in a total economic review of the cost of a building. Total economic calculations have so far been based on scenarios where workers’ performances have been assumed to be reduced between 1% and 10% on average on a yearly basis as a result of a sub-optimal indoor environment [1]. Such general statements, and the fact that building owners and employers know that the occupants of a workspace are different, hence also differently affected, is a barrier for the more widespread use of total economic building calculations in practice, which in addition to energy consumption, investment costs, maintenance costs, etc., take office worker performance also into account. It will be essential to improve total economic building calculations so that they fit each new individual building or renovation project. There is also a need then to make dynamic calculations so that the daily and seasonal variations of the indoor environment are properly accounted for when assessing performance.
On a routine basis, simulation tools are used in the building design phase to evaluate indoor environmental conditions and estimate the energy consumption of different design alternatives. However, the comparison of different designs may occur at a late stage in the design phase, thus reducing the significance of the simulation results and making it almost impossible to modify the design accordingly. By including the effect of employee performance in the evaluation of different designs, the total economic consequences would promote the possibility of placing more emphasis on simulation results and thus achieving a better building design.
In recent years, there has been increased focus on the way in which different indoor climate factors affect employee performance. A systematic review of all available data on the effects of temperature and air quality on health and performance was conducted by Fisk and Seppänen [2] and Seppänen et al. [3]. This work resulted in the development of initial dose–response relationships between selected indoor climate parameters and performance. So far, all attempts to derive economic estimates of the effect of indoor climate on performance have been very crude. The economic losses of a sub-optimal indoor environment have been calculated mostly at the national level, revealing the enormous economic potential of improving indoor environmental quality in commercial buildings [4]. However, with current knowledge, the benefit for individual companies of indoor environmental quality (IEQ) upgrades has been difficult to quantify.
This paper proposes a new method of assessing the effects of the indoor environment on office workers’ mental performance. The method is based on probabilistic knowledge of indoor climate variables and how they are inter-related. The platform for the method is the Bayesian Network (BN) theory. So far, BN has been used very little in the field of indoor climate, whereas its use in artificial intelligence and in medicine is well established, e.g. for estimating the risk of disease [5], [6] and [7]. In Naticchia et al., a BN is used as a multi-criteria decision tool to choose an optimal building design for buildings equipped with a roofpond [8].
Central complexity in predicting the effects of the indoor climate on humans relates not only to the number of factors that interact, but also to modelling the differences in human perception of the indoor climate. This complexity is handled by the BN by modelling a perceived causal relationship between indoor climate factors and human perception. Furthermore, probabilities are used to model the “weight” of the causal relationship so that a qualified assessment of the effects of indoor climate factors on human sensation and performance may be established. These probabilities (or weights) can be learned from observed data.
Section 2 of this paper presents a general approach to the way in which the performance of office employees can be estimated. In Section 3, a comparison between four different building designs located in northern Europe and two different building designs in Los Angeles, California, are used as examples to analyse the effects of temperature on the mental performance of office workers in a specific building. In general, in this paper, focus will be on the effects of temperature on the mental performance and not of other indoor climate factors.
The present paper introduces a Performance Index (Π) determined by a BN, which can be used to compare different building designs in terms of their estimated economic consequences, by including effects on occupant performance as well as energy use. The Performance Index is calculated using a BN as the platform so any uncertainties associated with human performance and perception can be included in the estimates of the overall Performance Index. The design examples compared in the paper indicate considerable benefits from improving the indoor thermal environment, particularly in the warm climate areas of the world with poor building designs.
