Effects of Energy Consumption on Indoor Air Quality
Continuous measurements and multivariate methods are applied in researching the effects of energy consumption on indoor air quality (IAQ) in a Finnish one-family house. Measured data used in this study was collected continuously in a house in Kuopio, Eastern Finland, during fourteen months long period. Consumption parameters measured were the consumptions of district heat, electricity and water. Indoor parameters gathered were temperature, relative humidity (RH), the concentrations of carbon dioxide (CO2) and carbon monoxide (CO) and differential air pressure. In this study, self-organizing map (SOM) and Sammon's mapping were applied to resolve the effects of energy consumption on indoor air quality. Namely, the SOM was qualified as a suitable method having a property to summarize the multivariable dependencies into easily observable two-dimensional map. Accompanying that, the Sammon's mapping method was used to cluster pre-processed data to find similarities of the variables, expressing distances and groups in the data. The methods used were able to distinguish 7 different clusters characterizing indoor air quality and energy efficiency in the study house. The results indicate, that the cost implications in euros of heating and electricity energy vary according to the differential pressure, concentration of carbon dioxide, temperature and season.
[1] N. Gershenfeld, S. Samouhos, and B. Nordman: Intelligent Infrastructure for energy efficiency," Science, vol. 372, pp. 1086-1088, Feb. 2010.
[2] R. J. Jackson, "Environment Meets Health, Again," Science, 315(5817), pp.1337, Mar. 2007.
[3] J. P. Holdren, "Energy and Sustainability," Science 315 (5813), pp. 737, Feb. 2007.
[4] R. Armstong and N. Spiller, "Synthetic Biology: Living quarters," Nature, 467, pp. 916-918, Oct. 2010.
[5] D. Butler, "Architects of a Low-energy Future," Nature 452, pp. 520-523, Apr. 2008.
[6] 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.
[7] 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.
[8] 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.
[9] T. E. Alhafany, 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.
[10] 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
[11] 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.
[12] 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.
[13] 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.
[14] M. Santamouris, G. Mihalakakou, P. Patargias, N. Gaitani, K. Sfakianaki, M. Papaglastra, C. Pavlou, P. Doukas, E. Primikiri, V. Geros, M. N. Assimakopoulos, R. Mitoula, S. Zerefos, "Using Intelligent clustering techniques to classify the energy performance of school buildings," Energy and Buildings, vol. 39, pp. 45-51, 2007.
[15] Z. Wang, Z. Bai, H. Ya, J. Zhang, T. Zhu, "Regulatory standards related to building energy conservation and indoor-air-quality during rapid urbanization in China," Energy and Buildings, vol. 36, pp. 1299-1308, 2004.
[16] J. Yao, N. Zhu, "Enhanced supervision strategies for effective reduction
of building energy consumption - A case study of Ningbo," Energy and Buildings, vol. 43, pp.2197-2202, 2011.
[17] A. Simoudi ,P. Kostarela, "Energy monitoring and conservation potential in school buildings in the C´ climate zone of Greece," Renewable Energy, vol. 43, pp. 289--296, 2009.
[18] A. Elkilani, W. Bouhamra, "Estimation of optimum requirements for indoor air quality and energy consumption in some residences in Kuwait," Environment International, vol. 27, pp.443-447, 2001.
[19] 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.
[20] T. Kohonen, "Self-organizing maps," 3rd ed., Springer-Verlag, Berlin
Heidelberg, 2001.
[21] J. MacQueen, "Some methods for classification and analysis of multivariate observations," Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297, 1967.
[22] D. Davies and D. A. Bouldin, "Cluster separation measure," IEEE Trans
Pattern Anal Mach Intell, vol. 2, pp. 224-7, 1979.
[23] J. W. Sammon, "A nonlinear mapping for data structure analysis," IEEE
Transactions on Computers 18: 401-409, 1969.
[1] N. Gershenfeld, S. Samouhos, and B. Nordman: Intelligent Infrastructure for energy efficiency," Science, vol. 372, pp. 1086-1088, Feb. 2010.
[2] R. J. Jackson, "Environment Meets Health, Again," Science, 315(5817), pp.1337, Mar. 2007.
[3] J. P. Holdren, "Energy and Sustainability," Science 315 (5813), pp. 737, Feb. 2007.
[4] R. Armstong and N. Spiller, "Synthetic Biology: Living quarters," Nature, 467, pp. 916-918, Oct. 2010.
[5] D. Butler, "Architects of a Low-energy Future," Nature 452, pp. 520-523, Apr. 2008.
[6] 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.
[7] 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.
[8] 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.
[9] T. E. Alhafany, 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.
[10] 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
[11] 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.
[12] 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.
[13] 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.
[14] M. Santamouris, G. Mihalakakou, P. Patargias, N. Gaitani, K. Sfakianaki, M. Papaglastra, C. Pavlou, P. Doukas, E. Primikiri, V. Geros, M. N. Assimakopoulos, R. Mitoula, S. Zerefos, "Using Intelligent clustering techniques to classify the energy performance of school buildings," Energy and Buildings, vol. 39, pp. 45-51, 2007.
[15] Z. Wang, Z. Bai, H. Ya, J. Zhang, T. Zhu, "Regulatory standards related to building energy conservation and indoor-air-quality during rapid urbanization in China," Energy and Buildings, vol. 36, pp. 1299-1308, 2004.
[16] J. Yao, N. Zhu, "Enhanced supervision strategies for effective reduction
of building energy consumption - A case study of Ningbo," Energy and Buildings, vol. 43, pp.2197-2202, 2011.
[17] A. Simoudi ,P. Kostarela, "Energy monitoring and conservation potential in school buildings in the C´ climate zone of Greece," Renewable Energy, vol. 43, pp. 289--296, 2009.
[18] A. Elkilani, W. Bouhamra, "Estimation of optimum requirements for indoor air quality and energy consumption in some residences in Kuwait," Environment International, vol. 27, pp.443-447, 2001.
[19] 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.
[20] T. Kohonen, "Self-organizing maps," 3rd ed., Springer-Verlag, Berlin
Heidelberg, 2001.
[21] J. MacQueen, "Some methods for classification and analysis of multivariate observations," Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297, 1967.
[22] D. Davies and D. A. Bouldin, "Cluster separation measure," IEEE Trans
Pattern Anal Mach Intell, vol. 2, pp. 224-7, 1979.
[23] J. W. Sammon, "A nonlinear mapping for data structure analysis," IEEE
Transactions on Computers 18: 401-409, 1969.
@article{"International Journal of Architectural, Civil and Construction Sciences:61974", author = "M. Raatikainen and J-P. Skön and M. Johansson and K. Leiviskä and M. Kolehmainen", title = "Effects of Energy Consumption on Indoor Air Quality", abstract = "Continuous measurements and multivariate methods are applied in researching the effects of energy consumption on indoor air quality (IAQ) in a Finnish one-family house. Measured data used in this study was collected continuously in a house in Kuopio, Eastern Finland, during fourteen months long period. Consumption parameters measured were the consumptions of district heat, electricity and water. Indoor parameters gathered were temperature, relative humidity (RH), the concentrations of carbon dioxide (CO2) and carbon monoxide (CO) and differential air pressure. In this study, self-organizing map (SOM) and Sammon's mapping were applied to resolve the effects of energy consumption on indoor air quality. Namely, the SOM was qualified as a suitable method having a property to summarize the multivariable dependencies into easily observable two-dimensional map. Accompanying that, the Sammon's mapping method was used to cluster pre-processed data to find similarities of the variables, expressing distances and groups in the data. The methods used were able to distinguish 7 different clusters characterizing indoor air quality and energy efficiency in the study house. The results indicate, that the cost implications in euros of heating and electricity energy vary according to the differential pressure, concentration of carbon dioxide, temperature and season.
", keywords = "Indoor air quality, Energy efficiency, Self-
organizing map, Sammon's mapping", volume = "6", number = "5", pages = "370-6", }
