Self-Organizing Maps
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
EnvSOM: a SOM algorithm conditioned on the environment for clustering and visualization
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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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.