| 書(shū)目名稱(chēng) | Data-Driven Modelling of Non-Domestic Buildings Energy Performance |
| 副標(biāo)題 | Supporting Building |
| 編輯 | Saleh Seyedzadeh,Farzad Pour Rahimian |
| 視頻video | http://file.papertrans.cn/264/263304/263304.mp4 |
| 概述 | 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 |
| 叢書(shū)名稱(chēng) | Green Energy and Technology |
| 圖書(shū)封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2021 |
| 關(guān)鍵詞 | Building Energy Performance; Building Energy Modelling; Data-Driven Modelling; Machine Learning; Energy |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-64751-3 |
| isbn_softcover | 978-3-030-64753-7 |
| isbn_ebook | 978-3-030-64751-3Series ISSN 1865-3529 Series E-ISSN 1865-3537 |
| issn_series | 1865-3529 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機(jī)版|小黑屋|
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