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Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions

:	Nuno M.M. Ramos, João M.P.Q. Delgado, Ricardo M.S.F. Almeida, Maria L. Simões, Sofia Manuel
:	Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions
:	Springer-Verlag
:	9783319222943
:	1
:	CHF 47.50
:

:	Bau- und Umwelttechnik
:	English

:	56
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

The main benefit of the book is that it explores available methodologies for both conducting in-situ measurements and adequately exploring the results, based on a case study that illustrates the benefits and difficulties of concurrent methodologies.

The case study corresponds to a set of 25 social housing dwellings where an extensive in situ measurement campaign was conducted. The dwellings are located in the same quarter of a city. Measurements included indoor temperature and relative humidity, with continuous log in different rooms of each dwelling, blower-door tests and complete outdoor conditions provided by a nearby weather station.

The book includes a variety of scientific and engineering disciplines, such as building physics, probability and statistics and civil engineering. It presents a synthesis of the current state of knowledge for benefit of professional engineers and scientists.

	Preface	6
	Acknowledgments	8
	Contents	9
	1 Introduction	11
	Abstract	11
	1.1 Motivation	11
	1.2 Methodology	12
	References	13
	2 Indoor Hygrothermal Conditions	14
	Abstract	14
	2.1 Involved Parameters	14
	2.1.1 Air Temperature	15
	2.1.2 Relative Humidity	15
	2.2 Standardized Methodologies	16
	2.3 Literature Review	19
	2.3.1 Building Performance Versus User Behaviour	19
	2.3.2 Building Performance Indicators	19
	References	20
	3 Data Mining Techniques	22
	Abstract	22
	3.1 Principles	22
	3.2 Basic Statistical Tools	22
	3.2.1 Descriptive Statistics	23
	3.2.2 Probability Distributions	23
	3.2.2.1 Discrete Probability Distributions	24
	3.2.2.2 Continuous Probability Distributions	24
	3.2.3 Correlation Matrices	25
	3.2.4 Multi-Way Frequency Tables	26
	3.3 Multivariate Data Techniques	27
	3.3.1 Principal Components and Factor Analysis	27
	3.3.1.1 Principal Components	28
	3.3.1.2 Factor Analysis	33
	3.3.2 Discriminant and Cluster Analysis	34
	3.3.2.1 Discriminant Analysis	34
	3.3.2.2 Cluster Analysis	34
	3.3.3 Multiple Regression Analysis	38
	3.3.4 Classification Trees	39
	References	39
	4 Case Study	40
	Abstract	40
	4.1 Sample Description	40
	4.2 In-Situ Campaign	40
	4.3 Measurement Results	41
	4.3.1 Outdoor Conditions	42
	4.3.2 Indoor Temperature	43
	4.3.3 Indoor Relative Humidity	44
	References	45
	5 Application of Data Mining Techniques	46
	Abstract	46
	5.1 Statistical Analysis of Data	46
	5.2 Multivariate Data Analysis	49
	6 Conclusions	55
	Abstract	55