Machine learning for building energy forecasting

Saleh Seyedzadeh, Farzad Pour Rahimian

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In recent years, Artificial Intelligence (AI) in general and Machine Learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This chapter provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

Original languageEnglish
Title of host publicationData-Driven Modelling of Non-Domestic Buildings Energy Performance
PublisherSpringer Science and Business Media Deutschland GmbH
Pages41-76
Number of pages36
ISBN (Electronic)9783030647513
ISBN (Print)9783030647506
DOIs
Publication statusPublished - 16 Jan 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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