Electronic Resource

Data-Driven Modelling of Non-Domestic Buildings Energy Performance

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2108000019	EB0005270	696 Sey d	Central Library (OPAC)	Available

Detail Information

Series Title

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Call Number

696 Sey d

Publisher

Switzerland : Springer Cham., 2021

Collation

xiv, 153p.:ill

Language

English

ISBN/ISSN

978-3-030-64751-3

Classification

696

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Energy and Environmental of Building

Specific Detail Info

-

Statement of Responsibility

BRF

Other version/related

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