Data-Driven Modelling of Non-Domestic Buildings Energy Performance
Supporting Building Retrofit Planning
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Beschreibung
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.
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.
Offers a framework to efficiently select machine learning models to forecast energy loads of buildings Develops an energy performance prediction model for non-domestic buildings Provides a case study showing the methodology at work
Autor*in
Saleh Seyedzadeh
Themen in »Data-Driven Modelling of Non-Domestic Buildings Energy Performance«
Building Energy Performance Building Energy Modelling Data-Driven Modelling Machine Learning Energy Prediction Deep Energy Retrofit Multi-Objective Optimisation Decision-Making System Energy Performance Indicator Building Energy Retrofit