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
PublisherSpringer Science and Business Media Deutschland GmbH
Pages99-109
Number of pages11
ISBN (Electronic)9783030647513
ISBN (Print)9783030647506
DOIs
Publication statusPublished - 2021

Publication series

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

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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