Modelling energy performance of non-domestic buildings

Saleh Seyedzadeh, Farzad Pour Rahimian

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter presents an energy performance prediction model for the UK non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic building energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provides a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space.

Original languageEnglish
Title of host publicationData-Driven Modelling of Non-Domestic Buildings Energy Performance
Subtitle of host publicationSupporting Building Retrofit Planning
Place of PublicationCham, Switzerland
PublisherSpringer
Chapter7
Pages111-133
Number of pages23
ISBN (Print)978-3-030-64750-6
DOIs
Publication statusPublished - 16 Jan 2021

Publication series

NameGreen Energy and Technology
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537

Keywords

  • modelling
  • building energy analysis
  • non-domestic building emission rate

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