國立虎尾科技大學 |

Data-Driven Modelling of Non-Domestic Buildings Energy Performance = Supporting Building Retrofit Planning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data-Driven Modelling of Non-Domestic Buildings Energy Performance/ by Saleh Seyedzadeh, Farzad Pour Rahimian.
其他題名:	Supporting Building Retrofit Planning /
作者:	Seyedzadeh, Saleh.
其他作者:	Pour Rahimian, Farzad.
面頁冊數:	XIV, 153 p. 48 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Sustainable architecture. -
電子資源:	https://doi.org/10.1007/978-3-030-64751-3
ISBN:	9783030647513

Data-Driven Modelling of Non-Domestic Buildings Energy Performance = Supporting Building Retrofit Planning /
Seyedzadeh, Saleh.

Data-Driven Modelling of Non-Domestic Buildings Energy PerformanceSupporting Building Retrofit Planning /[electronic resource] :by Saleh Seyedzadeh, Farzad Pour Rahimian. - 1st ed. 2021. - XIV, 153 p. 48 illus. in color.online resource. - Green Energy and Technology,1865-3537. - Green Energy and Technology,.

Introduction -- Building Energy Performance Assessment -- Machine Learning for Building Energy Forecasting -- Building Retrofit Planning -- Machine Learning Models for Prediction of Building Energy Performance -- Building Energy Data Driven Model Improved by Multi-Objective Optimisation -- Modelling Energy Performance of Non-Domestic Buildings.

This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.

ISBN: 9783030647513

Standard No.: 10.1007/978-3-030-64751-3doiSubjects--Topical Terms:

561955
Sustainable architecture.

LC Class. No.: NA2542.36

Dewey Class. No.: 720.47

Data-Driven Modelling of Non-Domestic Buildings Energy Performance = Supporting Building Retrofit Planning /
LDR:02919nam a22004095i 4500 001 1052013
003 DE-He213
005 20210921155753.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030647513 $9 978-3-030-64751-3
024 7 $a 10.1007/978-3-030-64751-3 $2 doi
035 $a 978-3-030-64751-3
050 4 $a NA2542.36
072 7 $a AMCR $2 bicssc
072 7 $a ARC018000 $2 bisacsh
072 7 $a AMCR $2 thema
082 0 4 $a 720.47 $2 23
100 1 $a Seyedzadeh, Saleh. $e author. $0 (orcid)0000-0001-6017-289X $1 https://orcid.org/0000-0001-6017-289X $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1356628
245 1 0 $a Data-Driven Modelling of Non-Domestic Buildings Energy Performance $h [electronic resource] : $b Supporting Building Retrofit Planning / $c by Saleh Seyedzadeh, Farzad Pour Rahimian.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XIV, 153 p. 48 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Green Energy and Technology, $x 1865-3537
505 0 $a Introduction -- Building Energy Performance Assessment -- Machine Learning for Building Energy Forecasting -- Building Retrofit Planning -- Machine Learning Models for Prediction of Building Energy Performance -- Building Energy Data Driven Model Improved by Multi-Objective Optimisation -- Modelling Energy Performance of Non-Domestic Buildings.
520 $a This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.
650 0 $a Sustainable architecture. $3 561955
650 0 $a Buildings—Design and construction. $3 1254621
650 0 $a Building. $3 557609
650 0 $a Construction. $3 1254179
650 0 $a Engineering, Architectural. $3 845652
650 0 $a Building construction. $3 1255346
650 0 $a Thermodynamics. $3 596513
650 0 $a Heat engineering. $3 681953
650 0 $a Heat transfer. $3 1085480
650 0 $a Mass transfer. $3 556853
650 1 4 $a Sustainable Architecture/Green Buildings. $3 1117322
650 2 4 $a Building Construction and Design. $3 1114677
650 2 4 $a Building Physics, HVAC. $3 1020217
650 2 4 $a Engineering Thermodynamics, Heat and Mass Transfer. $3 769147
700 1 $a Pour Rahimian, Farzad. $e author. $1 https://orcid.org/0000-0001-7443-4723 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1356629
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030647506
776 0 8 $i Printed edition: $z 9783030647520
776 0 8 $i Printed edition: $z 9783030647537
830 0 $a Green Energy and Technology, $x 1865-3529 $3 1254274
856 4 0 $u https://doi.org/10.1007/978-3-030-64751-3
912 $a ZDB-2-ENE
912 $a ZDB-2-SXEN
950 $a Energy (SpringerNature-40367)
950 $a Energy (R0) (SpringerNature-43717)