Abstract
Analyzing power consumption is important for economic and environmental reasons. Through the analysis of electrical variables, power could be saved and, therefore, better energy efficiency could be reached in buildings. The application of advanced data analysis helps to provide a better understanding, especially if it enables a joint and comparative analysis of different buildings which are influenced by common environmental conditions. In this paper, we present an approach to monitor and compare electrical consumption profiles of several buildings from the Campus of the University of León. The envSOM algorithm, a modification of the self-organizing map (SOM), is used to reduce the dimension of data and capture their electrical behaviors conditioned on the environment. After that, a Sammon’s mapping is used to visualize global, component-wise or environmentally conditioned similarities among the buildings. Finally, a clustering step based on k-means algorithm is performed to discover groups of buildings with similar electrical behavior.
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References
European Parliament: Directive 2010/31/EU of the European Parliament and of the Council of 19 may 2010 on the energy performance of buildings (recast). Official Journal of the European Union 53(L153) (2010)
Gershenfeld, N., Samouhos, S., Nordman, B.: Intelligent infrastructure for energy efficiency. Science 327(5969), 1086–1088 (2010)
Darby, S.: The effectiveness of feedback on energy consumption. Technical report, Environmental Change Institute. University of Oxford (2006)
Sforna, M.: Data mining in a power company customer database. Electric Power Systems Research 55(3), 201–209 (2000)
Chicco, G., Napoli, R., Piglione, F., Postolache, P., Scutariu, M., Toader, C.: Load pattern-based classification of electricity customers. IEEE Transactions on Power Systems 19(2), 1232–1239 (2004)
Figueiredo, V., Rodrigues, F., Vale, Z., Gouveia, J.: An electric energy consumer characterization framework based on data mining techniques. IEEE Transactions on Power Systems 20(2), 596–602 (2005)
Verdú, S., García, M., Senabre, C., Marín, A., Franco, F.: Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Transactions on Power Systems 21(4), 1672–1682 (2006)
Kohonen, T.: Self-Organizing Maps. Springer, New York (1995)
Alonso, S., Sulkava, M., Prada, M., Domínguez, M., Hollmén, J.: EnvSOM: A SOM algorithm conditioned on the environment for clustering and visualization. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 61–70. Springer, Heidelberg (2011)
Eckerson, W.W.: Three tier client/server architectures: Achieving scalability, performance, and efficiency in client/server applications. Open Information Systems 3(20), 46–50 (1995)
Kastner, W., Neugschwandtner, G., Soucek, S., Newmann, H.: Communication systems for building automation and control. Proceedings of the IEEE 93(6), 1178–1203 (2005)
Samad, T., Harp, S.A.: Self-organization with partial data. Network: Computation in Neural Systems 3, 205–212 (1992)
Fan, S., Chen, L., Lee, W.J.: Short-term load forecasting using comprehensive combination based on multimeteorological information. IEEE Transactions on Industry Applications 45(4), 1460–1466 (2009)
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proceedings of the IEEE 84(10), 1358–1384 (1996)
Vesanto, J.: SOM-based data visualization methods. Intelligent Data Analysis 3(2), 111–126 (1999)
Sammon Jr., J.W.: A non-linear mapping for data structure analysis. IEEE Transactions on Computers 18, 401–409 (1969)
Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction. Information Science and Statistics. Springer, Heidelberg (2007)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Intelligent Information Systems 17(2), 107–145 (2001)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM toolbox for matlab 5. Technical Report A57, Helsinki University of Technology (2000)
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Alonso, S., Domínguez, M., Prada, M.A., Sulkava, M., Hollmén, J. (2011). Comparative Analysis of Power Consumption in University Buildings Using envSOM. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_4
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DOI: https://doi.org/10.1007/978-3-642-24800-9_4
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