Data-Driven Modelling of Non-Domestic Buildings Energy Performance Supporting Building Retrofit Planning /

Data-Driven Modelling of Non-Domestic Buildings Energy Performance Supporting Building Retrofit Planning /

Podrobná bibliografie
Hlavní autoři: Seyedzadeh, Saleh (Autor), Pour Rahimian, Farzad (Autor)
Korporativní autor: SpringerLink (Online service)
Shrnutí:XIV, 153 p. 48 illus. in color.
text
Jazyk:angličtina
Vydáno: Cham : Springer International Publishing : Imprint: Springer, 2021.
Vydání:1st ed. 2021.
Edice:Green Energy and Technology,
Témata:
Sustainable architecture.
Buildings > Design and construction.
Buildings > Environmental engineering.
Thermodynamics.
Heat engineering.
Heat transfer.
Mass transfer.
Sustainable Architecture/Green Buildings.
Building Construction and Design.
Building Physics, HVAC.
Engineering Thermodynamics, Heat and Mass Transfer.
On-line přístup:https://doi.org/10.1007/978-3-030-64751-3
Médium: Elektronický zdroj Kniha
Obsah:
  • Introduction
  • 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.

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