LEADER 03057nam 2200493 450 001 9910481954203321 005 20210331060425.0 010 $a3-030-64751-X 024 7 $a10.1007/978-3-030-64751-3 035 $a(CKB)4100000011716906 035 $a(DE-He213)978-3-030-64751-3 035 $a(MiAaPQ)EBC6458038 035 $a(PPN)253250919 035 $a(EXLCZ)994100000011716906 100 $a20210331d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData-driven modelling of non-domestic buildings energy performance $esupporting building retrofit planning /$fSaleh Seyedzadeh, Farzad Pour Rahimian 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XIV, 153 p. 48 illus. in color.) 225 1 $aGreen energy and technology 311 $a3-030-64750-1 327 $aIntroduction -- 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. 330 $aThis book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings. 410 0$aGreen energy and technology. 606 $aBuildings$xEnergy conservation$xData processing 606 $aBuildings$xRetrofitting 615 0$aBuildings$xEnergy conservation$xData processing. 615 0$aBuildings$xRetrofitting. 676 $a696 700 $aSeyedzadeh$b Saleh$01229054 702 $aRahimian$b Farzad Pour 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910481954203321 996 $aData-driven modelling of non-domestic buildings energy performance$92853137 997 $aUNINA