Abstract
Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. In this paper we present two approaches to occupant location prediction based on association rule mining which allow prediction based on historical occupant movements and any available real time information, or based on recent occupant movements. We show how association rule mining can be adapted for occupant prediction and evaluate both approaches against existing approaches on two sets of real occupants.
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References
World Business Council for Sustainable Development, Energy Efficiency in Buildings: Facts and Trends - Full Report, (2008).
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Databases, 487–499, (1994).
Petzold, J., Pietzowski, A., Bagci, F., Trumler, W. and Ungerer, T.: Prediction of Indoor Movements Using Bayesian Networks. LoCA 2005, LNCS 3479, 211–222, (2005).
Vintan, L., Gellert, A., Petzold, J., and Ungerer, T.: Person Movement Prediction Using Neural Networks. Technical Report, Institute of Computer Science, University of Augsburg, (2004).
Petzold, J., Bagci, F., Trumler, W., Ungerer, T., and Vintan, L.: Global State Context Prediction Techniques Applied to a Smart Office Building. The Communication Networks and Distributed Systems Modeling and Simulation Conference, San Diego, CA, USA, (2004).
Gellert, A., Vintan, L.: Person Movement Prediction Using Hidden Markov Models. Studies in Informatics and, Control, Vol. 15, No.1 (2006).
Voigtmann, C., Sian Lun Lau, David, K.: A Collaborative Context Prediction Technique. Vehicular Technology Conference, (2011).
Eagle, N., Pentland, A. S.: Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology, Vol. 63, No. 7, 1057–1066, (2009).
Petzold, J.: Augsburg Indoor Location Tracking Benchmarks. Context Database, Institute of Pervasive Computing, University of Linz, Austria. http://www.soft.uni-linz.ac.at/Research/ContextDatabase/index.php, (2005).
Petzold, J., Bagci, F., Trumler, W., and Ungerer, T.: Comparison of Different Methods for Next Location Prediction. EuroPar 2006, LNCS 4128, 909–918, (2006).
Srikant, R., and Agrawal, R.: Mining Generalized Association Rules. Proceedings of the 21st Internationa1 Conference on Very Large Databases, 407–419, (1995).
Najib, W., Klepal, M., Wibowo, S.B.: MapUme: Scalable middleware for location aware computing applications. International Conf. on Indoor Positioning and Indoor Navigation (IPIN), (2011).
Mady, A. E.D., Provan, G., Ryan, C., Brown, K. N.: Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation. AAAI (2011).
Acknowledgments
This work is funded by Science Foundation Ireland under the grants ITOBO 07.SRC.I1170 and Insight 12/RC/2289
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© 2013 Springer International Publishing Switzerland
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Ryan, C., Brown, K.N. (2013). Predicting Occupant Locations Using Association Rule Mining. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_5
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DOI: https://doi.org/10.1007/978-3-319-02621-3_5
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