Building energy data-driven model improved by multi-objective optimisation

Saleh Seyedzadeh, Farzad Pour Rahimian

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter proposes a method for optimising ML models for forecasting both heating and cooling loads. The technique employs multi-objective optimisation with evolutionary algorithms to search the space of possible parameters. The proposed approach not only tunes single model to precisely predict building energy loads but also accelerates the process of model optimisation. The chapter utilises simulated building energy data to validate the proposed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies.

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
Chapter6
Pages99-109
Number of pages11
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

  • heating loads
  • cooling loads

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