Abstract
The analysis of the daily electricity consumption profile of a building and its correlation with environmental factors make it possible to estimate its electricity demand. As an alternative to the traditional correlation analysis, a new approach is proposed to provide a detailed and visual analysis of the correlations between consumption and environmental variables. Since consumption profiles are normally characterized by many electrical variables, i.e., a high dimensional space, it is necessary to apply dimensionality reduction techniques that enable a projection of these data onto an easily interpretable 2D space. In this paper, several dimensionality reduction techniques are compared in order to determine the most appropriate one for the stated purpose. Later, the proposed approach uses the chosen algorithm to analyze the profiles of two public buildings located at the University of León.
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Morán, A., Fuertes, J.J., Prada, M.A., Alonso, S., Barrientos, P., Díaz, I. (2012). Analysis of Electricity Consumption Profiles by Means of Dimensionality Reduction Techniques. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_16
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DOI: https://doi.org/10.1007/978-3-642-32909-8_16
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