Modelling Indoor Air Carbon Dioxide (CO2)Concentration using Neural Network
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.
[1] K. Arnold, "Sick building syndrome solutions," Professional Safety,
vol. 46, pp. 43-44, 2001.
[2] O. A. Seppänen, W. J. Fisk and M. J. Mendell, "Association of
ventilation rates and CO2 concentrations with health and other responses
in commercial and institutional buildings," Indoor Air, vol. 9, pp. 226-
252, 1999.
[3] Asumisterveysohje, Sosiaali- ja terveysministeriön oppaita, Sosiaali- ja
terveysministeriö, Oy Edita Ab, Helsinki, 2003 (in Finnish).
[4] D. Butler, "Architects of a Low-energy Future," Nature, 452, pp. 520-
523, Apr. 2008.
[5] R. Armstrong and N. Spiller, "Synthetic biology: Living quarters,"
Nature, 467, pp. 916-918, Oct. 2010.
[6] N. Gershenfeld, S. Samouhos, and B. Nordman: "Intelligent
Infrastructure for energy efficiency," Science, vol. 372, pp.1086-1088,
Feb. 2010.
[7] R. J. Jackson, "Environment Meets Health, Again," Science, 315(5817),
pp.1337, Mar. 2007.
[8] J. P. Holdren, "Energy and Sustainability," Science, 315(5813), pp. 737,
Feb. 2007.
[9] S. C. Sofuoglu, "Application of artificial neural networks to predict
prevalence of building-related symptoms in office buildings," Building
and Environment, vol. 43, pp. 1121-1126, 2007.
[10] H. Xie, F. Ma and Q. G. Bai, "Prediction of indoor air quality using
artificial neural networks," Fifth International Conference on Natural
Computation (ICNC '09), vol. 2, pp. 414-418, 2009.
[11] M. H. Kim, Y. S. Kim, J. J. Lim, J. T. Kim, S. W. Sung and C. K. Yoo,
"Data-driven prediction model of indoor air quality in an underground
space," Korean Journal of Chemical Engineering, vol. 27, pp. 1675-
1680, 2010.
[12] T. E. Alhanafy, F. Zaghlool and A. S. El Din Moustafa, "Neuro fuzzy
modeling scheme for the prediction of air pollution," Journal of
American Science, vol. 6, pp. 605-616, 2010.
[13] T. Lu and M. Viljanen, "Prediction of indoor temperature and relative
humidity using neural network models: model comparison," Neural
Computing & Applications, vol.18, pp. 345-357, 2009
[14] M. Kolehmainen, H. Martikainen, T. Hiltunen, and J. Ruuskanen,
"Forecasting air quality parameters using hybrid neural network
modelling," Environmental Monitoring and Assessment, vol. 65, pp.
277-286, 2000.
[15] M. Kolehmainen, H. Martikainen and J. Ruuskanen,
"Neural networks and periodic components used in air quality
forecasting," Atmospheric Environment, vol. 35, pp. 815-825, 2001.
[16] H. Niska, T. Hiltunen, M. Kolehmainen and J. Ruuskanen,
"Hybrid models for forecasting air pollution episodes,"
International Conference on Artificial Neural Networks and Genetic
Algorithms (ICANNGA'03), University Technical Institute of Roanne,
France April 23-25, 2003.
[17] J-P. Skön, O. Kauhanen and M. Kolehmainen, "Energy Consumption
and Air Quality Monitoring System," Proceedings of the 7th
International Conference on Intelligent Sensors, Sensor Networks and
Information Processing, pp. 163-167, Adelaide, Australia Dec. 6-9,
2011.
[18] S. Haykin, "Neural Networks-A Comprehensive Foundation," 2nd ed.,
New Jersey: Prentice-Hall Inc., 1999.
[19] R. Kohavi and F. Provost, "Glossary of terms," Machine Learning, vol.
30, pp. 271-274, 1998.
[20] C. J. Willmott, "Some Comments on the Evaluation of Model
Performance," Bulletin American Meteorological Society, vol. 63,
pp.1309-1313, 1982.
[1] K. Arnold, "Sick building syndrome solutions," Professional Safety,
vol. 46, pp. 43-44, 2001.
[2] O. A. Seppänen, W. J. Fisk and M. J. Mendell, "Association of
ventilation rates and CO2 concentrations with health and other responses
in commercial and institutional buildings," Indoor Air, vol. 9, pp. 226-
252, 1999.
[3] Asumisterveysohje, Sosiaali- ja terveysministeriön oppaita, Sosiaali- ja
terveysministeriö, Oy Edita Ab, Helsinki, 2003 (in Finnish).
[4] D. Butler, "Architects of a Low-energy Future," Nature, 452, pp. 520-
523, Apr. 2008.
[5] R. Armstrong and N. Spiller, "Synthetic biology: Living quarters,"
Nature, 467, pp. 916-918, Oct. 2010.
[6] N. Gershenfeld, S. Samouhos, and B. Nordman: "Intelligent
Infrastructure for energy efficiency," Science, vol. 372, pp.1086-1088,
Feb. 2010.
[7] R. J. Jackson, "Environment Meets Health, Again," Science, 315(5817),
pp.1337, Mar. 2007.
[8] J. P. Holdren, "Energy and Sustainability," Science, 315(5813), pp. 737,
Feb. 2007.
[9] S. C. Sofuoglu, "Application of artificial neural networks to predict
prevalence of building-related symptoms in office buildings," Building
and Environment, vol. 43, pp. 1121-1126, 2007.
[10] H. Xie, F. Ma and Q. G. Bai, "Prediction of indoor air quality using
artificial neural networks," Fifth International Conference on Natural
Computation (ICNC '09), vol. 2, pp. 414-418, 2009.
[11] M. H. Kim, Y. S. Kim, J. J. Lim, J. T. Kim, S. W. Sung and C. K. Yoo,
"Data-driven prediction model of indoor air quality in an underground
space," Korean Journal of Chemical Engineering, vol. 27, pp. 1675-
1680, 2010.
[12] T. E. Alhanafy, F. Zaghlool and A. S. El Din Moustafa, "Neuro fuzzy
modeling scheme for the prediction of air pollution," Journal of
American Science, vol. 6, pp. 605-616, 2010.
[13] T. Lu and M. Viljanen, "Prediction of indoor temperature and relative
humidity using neural network models: model comparison," Neural
Computing & Applications, vol.18, pp. 345-357, 2009
[14] M. Kolehmainen, H. Martikainen, T. Hiltunen, and J. Ruuskanen,
"Forecasting air quality parameters using hybrid neural network
modelling," Environmental Monitoring and Assessment, vol. 65, pp.
277-286, 2000.
[15] M. Kolehmainen, H. Martikainen and J. Ruuskanen,
"Neural networks and periodic components used in air quality
forecasting," Atmospheric Environment, vol. 35, pp. 815-825, 2001.
[16] H. Niska, T. Hiltunen, M. Kolehmainen and J. Ruuskanen,
"Hybrid models for forecasting air pollution episodes,"
International Conference on Artificial Neural Networks and Genetic
Algorithms (ICANNGA'03), University Technical Institute of Roanne,
France April 23-25, 2003.
[17] J-P. Skön, O. Kauhanen and M. Kolehmainen, "Energy Consumption
and Air Quality Monitoring System," Proceedings of the 7th
International Conference on Intelligent Sensors, Sensor Networks and
Information Processing, pp. 163-167, Adelaide, Australia Dec. 6-9,
2011.
[18] S. Haykin, "Neural Networks-A Comprehensive Foundation," 2nd ed.,
New Jersey: Prentice-Hall Inc., 1999.
[19] R. Kohavi and F. Provost, "Glossary of terms," Machine Learning, vol.
30, pp. 271-274, 1998.
[20] C. J. Willmott, "Some Comments on the Evaluation of Model
Performance," Bulletin American Meteorological Society, vol. 63,
pp.1309-1313, 1982.
@article{"International Journal of Earth, Energy and Environmental Sciences:60757", author = "J-P. Skön and M. Johansson and M. Raatikainen and K. Leiviskä and M. Kolehmainen", title = "Modelling Indoor Air Carbon Dioxide (CO2)Concentration using Neural Network", 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.", keywords = "Indoor air quality, Modelling, Neural networks", volume = "6", number = "1", pages = "39-5", }
