Data-Driven Modelling of Non-Domestic Buildings Energy Performance - Supporting Building Retrofit Planning (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Green Energy and Technology
Release date:	2022
First published:	2021
Authors:	Saleh Seyedzadeh • Farzad Pour Rahimian
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	153
Edition:	1st ed. 2021
ISBN-13:	978-3-03-064753-7
Categories:	Books > Arts & Architecture > Architecture > Architectural structure & design Books > Professional & Technical > Mechanical engineering & materials > Materials science > Engineering thermodynamics Books > Professional & Technical > Civil engineering, surveying & building > Building construction & materials > General
LSN:	3-03-064753-6
Barcode:	9783030647537

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