Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/104046
Author(s): Nuno M. M. Ramos
Ricardo M. S. F. Almeida
M. Lurdes Simões
Pedro F. Pereira
Title: Knowledge discovery of indoor environment patterns in mild climate countries based on data mining applied to in-situ measurements
Issue Date: 2017
Abstract: Temperature and relative humidity values, from a sample of 24 flats with homogeneous architectural features and social strata, were continuously measured during the heating season and a typical summer period. The results proved the existence of discomfort during the heating season, revealing energy poverty patterns, but at the same time sensible differences that could only be explained by user actions. This led to a deeper analysis of the data in search of relevant patterns and causes for the inhomogeneity. A methodology for exploring the resulting large data set is proposed in the present paper, based on the application of data mining techniques. The calculation of meaningful percentiles of hygrothermal variables, followed by the application of a principal components analysis, allowed for the cluster analysis to the flats. As a result, clusters with a specific hygrothermal pattern were found, which meant that the factors leading to such performance could be explored, thus revealing the importance of users and their behaviour.
URI: https://hdl.handle.net/10216/104046
Document Type: Artigo em Revista Científica Internacional
Rights: restrictedAccess
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

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