Abstract
In this work we employ ensemble classifiers for the problem of nonintrusive appliance load monitoring. In practical scenarios the question arises how to efficiently and automatically learn statistical models for appliance recognition, which is an important step for various problems in process recognition, healthcare, and energy consulting. This work is an application study that analyzes multi-class support vector machines (SVMs), and K-nearest neighbors (KNN) in the problem domain of automatically recognizing appliances. By combining two types of classifiers with varying parameterizations to ensembles, we reduce the classification error, and increase the robustness of the classifier. In the experimental part we consider a field study with household appliances, and compare the classifiers w.r.t. various training set and neighborhood sizes. It turns out that the ensembles belong to the best classifiers in all training set scenarios.
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.
Similar content being viewed by others
References
Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)
Baranski, M., Voss, J.: Genetic algorithm for pattern detection in NIALM systems. In: IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3462–3468 (2004)
Chang, H.-H., Yang, H.-T., Lin, C.-L.: Load Identification in Neural Networks for a Non-Intrusive Monitoring of Industrial Electrical Loads. In: Shen, W., Yong, J., Yang, Y., Barthès, J.-P.A., Luo, J. (eds.) CSCWD 2007. LNCS, vol. 5236, pp. 664–674. Springer, Heidelberg (2008)
Corchado, E., Abraham, A., de Carvalho, A.C.P.L.F.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)
Corchado, E., Graña, M., Wozniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)
Cover, T., Hart, P.: Nearest neighbor pattern classification 13, 21–27 (1967)
Fung, G.P.C., Yu, J.X., Wang, H., Cheung, D.W., Liu, H.: A Balanced Ensemble Approach to Weighting Classifiers for Text Classification. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 869–873. Springer, Heidelberg (2006)
Gieseke, F., Kramer, O., Airola, A., Pahikkala, T.: Speedy Local Search for Semi-Supervised Regularized Least-Squares. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS, vol. 7006, pp. 87–98. Springer, Heidelberg (2011)
Hart, G.W.: Nonintrusive appliance load monitoring 80(12), 1870–1891 (1992)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Berlin (2009)
Lin, Y.-H., Tsai, M.-S.: Applications of hierarchical support vector machines for identifying load operation in nonintrusive load monitoring systems. In: Intelligent Control and Automation (WCICA), pp. 688–693 (2011)
Opitz, D.W., Maclin, R.: Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research (JAIR) 11, 169–198 (1999)
Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., Abowd, G.D.: At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award). In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 271–288. Springer, Heidelberg (2007)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann (1999)
Woods, K., Kegelmeyer, W.P., Bowyer, K.W.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4), 405–410 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kramer, O. et al. (2012). On Ensemble Classifiers for Nonintrusive Appliance Load Monitoring. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-28942-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28941-5
Online ISBN: 978-3-642-28942-2
eBook Packages: Computer ScienceComputer Science (R0)