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Learning Control of Indoor Air

Researchers: Timo Kostiainen, Jouko Lampinen and Markus Varsta


The project is financed by TEKES and carried out in co-operation with the Finnish Institute of Occupational Health.

The quality of indoor air is a major contributor to work atmosphere and effectivity in general and conversely perceived problems in air quality may well be symptoms of problems not at all related to inside air, consequently the aim of this project is two fold. First we aim to identify problems in the air quality that cause unpleasantness adding to work related stress and thus reduce effectiveness. Once the problems are pinpointed the air conditioning system may be adjusted accordingly. On the other hand we also want to identify problems in the working atmosphere, which problems are frequently linked with poor quality of air but in fact are caused by infected personal relations, too high work load or other causes of stress.

Our part of the project concentrates on developing statistical models for the analysis of dependencies between the measured air quality data such as temperature and relative humidity, and information regarding the perceived indoor air quality gathered with questionnaires. In the project we have aplied Self-Organizing Maps as one tool, together with factor analysis, Bayesian generalized linear models and non-linear regression by MLP, for clustering the variables related to physical and chemical measurements, psychosocial working environment and occupants' medical records.


 
Figure 8
Figure 8:   A self-organizing map that can be used to find dependencies between occupants' reactions to their work environment and measured attributes of indoor air quality. Large values on the color scale indicate dissatisfaction.


next up previous contents
Next: Internet Services Modelling Up: Computational Information Processing Previous: Urban Area Classification Based
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