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

Author:   Nuno M.M. Ramos ,  João M.P.Q. Delgado ,  Ricardo M.S.F. Almeida ,  Maria L. Simões
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2016
ISBN:  

9783319222936


Pages:   48
Publication Date:   03 September 2015
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions


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Overview

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.

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Author:   Nuno M.M. Ramos ,  João M.P.Q. Delgado ,  Ricardo M.S.F. Almeida ,  Maria L. Simões
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2016
Dimensions:   Width: 15.50cm , Height: 0.30cm , Length: 23.50cm
Weight:   1.066kg
ISBN:  

9783319222936


ISBN 10:   3319222937
Pages:   48
Publication Date:   03 September 2015
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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