Language:
English
繁體中文
Help
圖資館首頁
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Data-driven modelling of non-domesti...
~
Pour Rahimian, Farzad.
Data-driven modelling of non-domestic buildings energy performancesupporting building retrofit planning /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Data-driven modelling of non-domestic buildings energy performanceby Saleh Seyedzadeh, Farzad Pour Rahimian.
Reminder of title:
supporting building retrofit planning /
Author:
Seyedzadeh, Saleh.
other author:
Pour Rahimian, Farzad.
Published:
Cham :Springer International Publishing :2021.
Description:
xiv, 153 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
BuildingsEnergy conservation
Online resource:
https://doi.org/10.1007/978-3-030-64751-3
ISBN:
9783030647513$q(electronic bk.)
Data-driven modelling of non-domestic buildings energy performancesupporting building retrofit planning /
Seyedzadeh, Saleh.
Data-driven modelling of non-domestic buildings energy performance
supporting building retrofit planning /[electronic resource] :by Saleh Seyedzadeh, Farzad Pour Rahimian. - Cham :Springer International Publishing :2021. - xiv, 153 p. :ill., digital ;24 cm. - Green energy and technology,1865-3529. - Green energy and technology..
Introduction -- Building Energy Performance Assessment -- Machine Learning for Building Energy Forecasting -- Building Retrofit Planning -- Machine Learning Models for Prediction of Building Energy Performance -- Building Energy Data Driven Model Improved by Multi-Objective Optimisation -- Modelling Energy Performance of Non-Domestic Buildings.
This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.
ISBN: 9783030647513$q(electronic bk.)
Standard No.: 10.1007/978-3-030-64751-3doiSubjects--Topical Terms:
890152
Buildings
--Energy conservation
LC Class. No.: TJ163.5.B84 / S494 2021
Dewey Class. No.: 333.796217
Data-driven modelling of non-domestic buildings energy performancesupporting building retrofit planning /
LDR
:02581nmm a2200337 a 4500
001
597057
003
DE-He213
005
20210625153024.0
006
m d
007
cr nn 008maaau
008
211019s2021 sz s 0 eng d
020
$a
9783030647513$q(electronic bk.)
020
$a
9783030647506$q(paper)
024
7
$a
10.1007/978-3-030-64751-3
$2
doi
035
$a
978-3-030-64751-3
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
TJ163.5.B84
$b
S494 2021
072
7
$a
AMCR
$2
bicssc
072
7
$a
ARC018000
$2
bisacsh
072
7
$a
AMCR
$2
thema
082
0 4
$a
333.796217
$2
23
090
$a
TJ163.5.B84
$b
S519 2021
100
1
$a
Seyedzadeh, Saleh.
$3
890150
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.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2021.
300
$a
xiv, 153 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Green energy and technology,
$x
1865-3529
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
Buildings
$x
Energy conservation
$x
Data processing.
$3
890152
650
0
$a
Buildings
$x
Retrofitting.
$3
801406
650
0
$a
Buildings
$x
Repair and reconstruction.
$3
213796
650
0
$a
Green technology.
$3
263096
650
0
$a
Sustainable architecture.
$3
210599
650
1 4
$a
Sustainable Architecture/Green Buildings.
$3
766538
650
2 4
$a
Building Construction and Design.
$3
761394
650
2 4
$a
Building Physics, HVAC.
$3
675341
650
2 4
$a
Engineering Thermodynamics, Heat and Mass Transfer.
$3
338996
700
1
$a
Pour Rahimian, Farzad.
$3
890151
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer Nature eBook
830
0
$a
Green energy and technology.
$3
558129
856
4 0
$u
https://doi.org/10.1007/978-3-030-64751-3
950
$a
Energy (SpringerNature-40367)
based on 0 review(s)
ALL
電子館藏
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
000000195787
電子館藏
1圖書
電子書
EB TJ163.5.B84 S519 2021 2021
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Multimedia file
https://doi.org/10.1007/978-3-030-64751-3
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login