Data-Driven Modelling of Non-Domestic Buildings Energy Performance: Supporting Building Retrofit Planning (1st ed. 2021) (Green Energy and Technology)

By: Saleh Seyedzadeh and Farzad Pour Rahimian

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Synopsis: 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.

Book Quality:: Publisher Quality
ISBN-13:: 9783030647513
Related ISBNs:: 9783030647506
Publisher:: Springer International Publishing
Date of Addition:: 02/16/21
Copyrighted By:: Springer
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Science, Art and Architecture, Technology, Earth Sciences
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.