Data-driven modelling of non-domestic buildings energy performance : supporting building retrofit planning / Saleh Seyedzadeh, Farzad Pour Rahimian.
Seyedzadeh, Saleh, author; Pour Rahimian, Farzad, author
2021
TJ163.5.B84
Available Online

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Title Data-driven modelling of non-domestic buildings energy performance : supporting building retrofit planning / Saleh Seyedzadeh, Farzad Pour Rahimian.
Author Seyedzadeh, Saleh, author
ISBN 303064751X (electronic book)
9783030647513 (electronic bk.)
9783030647506
3030647501
DOI https://doi.org/10.1007/978-3-030-64751-3
Published Cham, Switzerland : Springer, [2021]
Language English
Description 1 online resource (xiv, 153 pages) : color illustrations
Item Number 10.1007/978-3-030-64751-3 doi
Call Number TJ163.5.B84
Dewey Decimal Classification 333.79/6217
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 4, 2021).
Added Author Pour Rahimian, Farzad, author
Series Green energy and technology, 1865-3529
Available in Other Form Print version: 9783030647506
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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.

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