Abstract
Nowadays, due to the great advent of sensor technology, the data of all appliances in a house can be collected easily. However, with a huge amount of appliance usage log data, it is not an easy task for residents to visualize how the appliances are used. Mining algorithms is necessary to discover appliance usage patterns that capture representative usage behavior of appliances. If some of our representative patterns of appliance electricity usages are available, we may be able to adapt our usage behaviors to conserve the energy easily. In this paper, we introduce (i) two types of usage patterns which capture the representative usage behaviors of appliances in a smart home environment and (ii) the corresponding algorithms for discovering usage patterns efficiently. Finally, we apply our algorithms on a real-world dataset to show the practicability of usage pattern mining.
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
Preview
Unable to display preview. Download preview PDF.
References
Aritoni, O., Negru, V.: A Methodology for Household Appliances Behavior Rec-ognition in AmI Systems Integration. In: 7th International Conference on Auto-matic and Autonomous Systems (ICAS 2011), pp. 175–178 (2011)
Chen, F., Dai, J., Wang, B., Sahu, S., Naphade, M., Lu, C.T.: Activity Analysis Based on Low Sample Rate Smart Meters. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011), pp. 240–248 (2011)
Farinaccio, L., Zmeureanu, R.: Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings 30(3), 245–259 (1999)
Goncalves, H., Ocneanu, A., Bergés, M.: Unsupervised disaggregation of appliances using aggregated consumption data. In: KDD Workshop on Data Mining Applications in Sustainability (SustKDD 2011) (2011)
Ito, M., Uda, R., Ichimura, S., Tago, K., Hoshi, T., Matsushita, Y.: A method of appliance detection based on features of power waveform. In: 4th IEEE Symposium on Applications and the Internet (SAINT 2004), pp. 291–294 (2004)
Kato, T., Cho, H.S., Lee, D., Toyomura, T., Yamazaki, T.: Appliance recognition from electric current signals for information-energy integrated network in home environments. In: Mokhtari, M., Khalil, I., Bauchet, J., Zhang, D., Nugent, C. (eds.) ICOST 2009. LNCS, vol. 5597, pp. 150–157. Springer, Heidelberg (2009)
Kolter, J.Z., Johnson, M.J.: REDD: A public data set for energy disaggregation research. In: KDD Workshop on Data Mining Applications in Sustainability (SustKDD 2011) (2011)
Kim, H., Marwah, M., Arlitt, M., Lyon, G., Han, J.: Unsupervised disaggregation of low frequency power measurements. In: 11th SIAM International Conference on Data Mining (SDM 2011), pp. 747–758 (2011)
Lin, G., Lee, S., Hsu, J., Jih, W.: Applying power meters for appliance recognition on the electric panel. In: 5th IEEE Conference on Industrial Electronics and Applications (ISIEA 2010), pp. 2254–2259 (2010)
Matthews, H., Soibelman, L., Berges, M., Goldman, E.: Automatically disaggre-gating the total electrical load in residential buildings: a profile of the required solution. In: Intelligent Computing in Engineering, pp. 381–389 (2008)
Prudenzi, A.: A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel. In: IEEE Power Engineering Society Winter Meeting, vol. 2, pp. 491–496 (2002)
Suzuki, K., Inagaki, S., Suzuki, T., Nakamura, H., Ito, K.: Nonintrusive appliance load monitoring based on integer programming. In: International Conference on Instrumentation, Control and Information Technology, pp. 2742–2747 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, YC., Ko, YL., Peng, WC., Lee, WC. (2013). Mining Appliance Usage Patterns in Smart Home Environment. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-37453-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37452-4
Online ISBN: 978-3-642-37453-1
eBook Packages: Computer ScienceComputer Science (R0)