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
Subtitle of host publicationSupporting Building Retrofit Planning
Place of PublicationCham, Switzerland
PublisherSpringer
Chapter4
Pages41-76
Number of pages36
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

  • building energy forecasting
  • building energy analysis
  • building energy consumption

Fingerprint Dive into the research topics of 'Machine learning for building energy forecasting'. Together they form a unique fingerprint.

Cite this