A review of data-driven building energy consumption prediction studies

Kadir Amasyali, Nora M. El-Gohary

Research output: Research - peer-reviewReview article

Abstract

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

LanguageEnglish (US)
Pages1192-1205
Number of pages14
JournalRenewable and Sustainable Energy Reviews
Volume81
DOIs
StatePublished - 2018

Fingerprint

Energy utilization
Learning algorithms
Learning systems
Conservation
Planning

Keywords

  • Building energy
  • Data-driven methods
  • Energy consumption prediction
  • Machine learning

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

A review of data-driven building energy consumption prediction studies. / Amasyali, Kadir; El-Gohary, Nora M.

In: Renewable and Sustainable Energy Reviews, Vol. 81, 2018, p. 1192-1205.

Research output: Research - peer-reviewReview article

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