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Analysing Events and Anomalies in Indoor Air Quality Using Self-Organizing Maps
This paper presents an overview of an indoor air quality data analysis, using self-organizing maps. The aim of the study was to research quality variations in indoor air, and the suitability of the used method for large scale data analysis of indoor air quality. Research was conducted in a six floor apartment building (built in the 80’ies) located in Kuopio, Finland, from January to May 2011. Quality data were collected continuously in 6 apartments from 10 rooms at the 2nd and the 6th floors, using an energy consumption and indoor air quality monitoring system. Three of the six research apartments were located on the 2nd floor and the other three on the 6th floor. At first the indoor air quality data were modelled using the SOM-algorithm. Next, the neuron reference vectors of the formed map were clustered to reveal dominating elements of each territory of the map. The results indicated that the method presented in this paper is an efficient way to analyse indoor air quality. Results indicated also that problems with indoor air quality occur more often during the wintertime in buildings utilizing mechanical exhaust ventilation. In particular, elevated CO2 concentrations indicate poor air quality in the bedrooms.
Clustering, Indoor air quality, Self-organizing map, Neural networks
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