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 /

Bibliografische gegevens
Hoofdauteurs: Seyedzadeh, Saleh (Auteur), Pour Rahimian, Farzad (Auteur)
Coauteur: SpringerLink (Online service)
Samenvatting:XIV, 153 p. 48 illus. in color.
text
Taal:Engels
Gepubliceerd in: Cham : Springer International Publishing : Imprint: Springer, 2021.
Editie:1st ed. 2021.
Reeks:Green Energy and Technology,
Onderwerpen:
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.
Online toegang:https://doi.org/10.1007/978-3-030-64751-3
Formaat: Elektronisch Boek
Inhoudsopgave:
  • 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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