Data-driven modelling of non-domestic buildings energy performance: supporting building retrofit planning
Auteur :
Seyedzadeh, Saleh / Pour Rahimian, Farzad
Éditeur :
Springer Nature Switzerland AG
ISBN :
9783030647537
Date de publication :
16 janv. 2022
Dimensions :
23,5 x 15,5 cm
Langue :
Anglais
Pays d'origine :
Suisse
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.