Abstract
The highest cause of energy consumption in buildings is due to ’Heating, Ventilation, and Air Conditioning’ (HVAC) systems. However, a large number of interconnected variables are involved in the control of these systems, so conventional analysis approaches are difficult. For that reason, data analysis by means of dimensionality reduction techniques can be a useful approach to address energy efficiency in buildings. In this paper, a method is proposed to visualize the relevant features of a heating system and its behavior and to help finding correlations between temporal, production and distribution variables. It uses a modification of the self-organizing map. The proposed approach is applied to a real building at the University of León.
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Barrientos, P. et al. (2013). Analysis of Heating Systems in Buildings Using Self-Organizing Maps. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_38
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DOI: https://doi.org/10.1007/978-3-642-41013-0_38
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