Abstract
The use of multivariate methods is popular in indoor air quality applications such as prediction of indoor air quality, ventilation control and classify comfort Indoor air quality data used in this study was collected continuously in a family house in Kuopio, Eastern Finland, during ten months long period. Indoor parameters were temperature, relative humidity (RH), the concentrations of carbon dioxide (CO2), TVOC and carbon monoxide (CO) and differential air pressure and particle counts(0.5 um and 2.5 um). In this study, self-organizing map (SOM) were applied to indoor air quality data for define correlation between of non-linear variables. The SOM was qualified as a suitable method having a property to summarize the multivariable dependencies into easily observable two-dimensional map. In addition, this paper presents the development of adaptive indoor air quality control for co-learning smart house.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Salo, P.M., Sever, M.L., Zeldin, D.C.: Indoor allergens in school and day care environments. J. Allergy Clin. Immunol. 124(2), 185–192 (2009)
Asumisterveysohje. Sosiaali- ja terveysministeriön oppaita. 2003:1, Sosiaali- ja terveysministeriö, Oy Edita Ab, Helsinki (in Finnish)
Daisey, J.M., Angell, W.J., Apte, M.G.: Indoor air quality, ventilation and health symptoms in schools: an analysis of existing information. Indoor Air 13(1), 53–64 (2003)
Clements-Croome, D.J., et al.: Ventilation rates in schools. Build. Environ. 43(3), 362–367 (2008)
Becker, R., Goldberger, I., Paciuk, M.: Improving energy performance of school buildings while ensuring indoor air quality ventilation. Build. Environ. 42(9), 3261–3276 (2007)
Dascalaki, E.G., Sermpetzoglou, V.G.: Energy performance and indoor environmental quality in Hellenic schools. Energy Build. 43(2), 718–727 (2011)
European Commission. Report 19: Total volatile organic compounds (TVOC) in indoor air quality investigations. Office for Official Publications of the European Communities (1997). ISBN 92-828-1078-X
Sisäilmastoluokitus, 2008 Sisäilmayhdistysry, Rakennustietosäätiö RTS, Asunto-, toimitila- ja rakennuttajaliitto RAKLI, Suomen Arkkitehtiliitto SAFA, Suunnittelu- ja konsulttitoimistojen liitto SKOL (2008). (in Finnish)
Sofuoglu, S.C.: Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings. Build. Environ. 43(6), 1121–1126 (2008)
Xie, H., Ma, F., Bai, Q.: Prediction of indoor air quality using artificial neural networks. In: Fifth International Conference on Natural Computation, ICNC 2009, vol. 2 IEEE (2009)
Lu, T., Viljanen, M.: Prediction of indoor temperature and relative humidity using neural network models: model comparison. Neural Comput. Appl. 18(4), 345–357 (2009)
Kolehmainen, M., et al.: Forecasting air quality parameters using hybrid neural network modelling. In: Urban Air Quality: Measurement, Modelling and Management. Springer, Netherlands, pp. 277–286 (2000)
Kolehmainen, M., Martikainen, H., Ruuskanen, J.: Neural networks and periodic components used in air quality forecasting. Atmos. Environ. 35(5), 815–825 (2001)
Niska, H., et al.: Hybrid models for forecasting air pollution episodes. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna (2003)
Skon, J., Kauhanen, O., Kolehmainen, M.: Energy consumption and air quality monitoring system. In: 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE (2011)
Skön, J.P., et al.: Wireless building monitoring and control system. World Acad. Sci. Eng. Tech. 65, 706–711 (2012)
Kohonen, T.: Self-organizing maps, vol. 30. Springer, Heidelberg (2001)
Acknowledgments
This research work was funded by the European Union (EU) Artemis projects Adaptive Cooperative Control in Urban (Sub)Systems (ACCUS) and Design, Monitoring, and Operation of Adaptive Networked Embedded Systems (DEMANES). For financial support, the authors would like to thank the European Commission, as well as, authors would like to thank research group of Environmental Informatics, University of Eastern Finland, for providing the indoor air quality data for this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Johansson, M., Haataja, K., Kolehmainen, M., Toivanen, P. (2015). Design of a Novel Adaptive Indoor Air Quality Control for Co-learning Smart House. In: Kanjo, E., Trossen, D. (eds) Sensor Systems and Software. S-CUBE 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-319-17136-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-17136-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17135-7
Online ISBN: 978-3-319-17136-4
eBook Packages: Computer ScienceComputer Science (R0)