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@article{(International Science Index):http://waset.org/publications/11723,
  title    = {Modelling Indoor Air Carbon Dioxide (CO2)Concentration using Neural Network},
  author    = {J-P. Skön and  M. Johansson and  M. Raatikainen and  K. Leiviskä and  M. Kolehmainen},
  country   = {},
  abstract  = {The use of neural networks is popular in various
building applications such as prediction of heating load, ventilation
rate and indoor temperature. Significant is, that only few papers deal
with indoor carbon dioxide (CO2) prediction which is a very good
indicator of indoor air quality (IAQ). In this study, a data-driven
modelling method based on multilayer perceptron network for indoor
air carbon dioxide in an apartment building is developed.
Temperature and humidity measurements are used as input variables
to the network. Motivation for this study derives from the following
issues. First, measuring carbon dioxide is expensive and sensors
power consumptions is high and secondly, this leads to short
operating times of battery-powered sensors. The results show that
predicting CO2 concentration based on relative humidity and
temperature measurements, is difficult. Therefore, more additional
information is needed.},
    journal   = {International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering},  volume    = {6},
  number    = {1},
  year      = {2012},
  pages     = {37 - 41},
  ee        = {http://waset.org/publications/11723},
  url       = {http://waset.org/Publications?p=61},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 61, 2012},