001434235 000__ 03939cam\a2200637\i\4500
001434235 001__ 1434235
001434235 003__ OCoLC
001434235 005__ 20230309003717.0
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001434235 007__ cr\nn\nnnunnun
001434235 008__ 210115s2021\\\\sz\a\\\\ob\\\\000\0\eng\d
001434235 019__ $$a1232031470$$a1232281350$$a1236270234
001434235 020__ $$a303064751X$$q(electronic book)
001434235 020__ $$a9783030647513$$q(electronic bk.)
001434235 020__ $$z9783030647506
001434235 020__ $$z3030647501
001434235 0247_ $$a10.1007/978-3-030-64751-3$$2doi
001434235 035__ $$aSP(OCoLC)1238205352
001434235 040__ $$aDCT$$beng$$erda$$epn$$cDCT$$dEBLCP$$dSFB$$dOCLCO$$dGW5XE$$dYDX$$dN$T$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ$$dOCLCO$$dOCLCQ
001434235 049__ $$aISEA
001434235 050_4 $$aTJ163.5.B84
001434235 08204 $$a333.79/6217$$223
001434235 1001_ $$aSeyedzadeh, Saleh,$$eauthor$$1https://orcid.org/0000-0001-6017-289X
001434235 24510 $$aData-driven modelling of non-domestic buildings energy performance :$$bsupporting building retrofit planning /$$cSaleh Seyedzadeh, Farzad Pour Rahimian.
001434235 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001434235 300__ $$a1 online resource (xiv, 153 pages) :$$bcolor illustrations
001434235 336__ $$atext$$btxt$$2rdacontent
001434235 337__ $$acomputer$$bc$$2rdamedia
001434235 338__ $$aonline resource$$bcr$$2rdacarrier
001434235 347__ $$atext file
001434235 347__ $$bPDF
001434235 4901_ $$aGreen energy and technology,$$x1865-3529
001434235 504__ $$aIncludes bibliographical references.
001434235 5050_ $$aIntroduction -- Building Energy Performance Assessment -- Machine Learning for Building Energy Forecasting -- Building Retrofit Planning -- Machine Learning Models for Prediction of Building Energy Performance -- Building Energy Data Driven Model Improved by Multi-Objective Optimisation -- Modelling Energy Performance of Non-Domestic Buildings.
001434235 506__ $$aAccess limited to authorized users.
001434235 520__ $$aThis book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.
001434235 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 4, 2021).
001434235 650_0 $$aBuildings$$xEnergy conservation$$xData processing.
001434235 650_0 $$aBuildings$$xRetrofitting.
001434235 650_0 $$aBuildings$$xRepair and reconstruction.
001434235 650_0 $$aGreen technology.
001434235 650_0 $$aSustainable architecture.
001434235 650_6 $$aConstructions$$xÉconomies d'énergie$$xInformatique.
001434235 650_6 $$aConstructions$$xAmélioration.
001434235 650_6 $$aTechnologie de protection de l'environnement.
001434235 650_6 $$aArchitecture durable.
001434235 655_0 $$aElectronic books.
001434235 7001_ $$aPour Rahimian, Farzad,$$eauthor$$1https://orcid.org/0000-0001-7443-4723
001434235 77608 $$iPrint version:$$z9783030647506
001434235 830_0 $$aGreen energy and technology,$$x1865-3529
001434235 852__ $$bebk
001434235 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-64751-3$$zOnline Access$$91397441.1
001434235 909CO $$ooai:library.usi.edu:1434235$$pGLOBAL_SET
001434235 980__ $$aBIB
001434235 980__ $$aEBOOK
001434235 982__ $$aEbook
001434235 983__ $$aOnline
001434235 994__ $$a92$$bISE