A review of data-driven building energy consumption prediction studies

Kadir Amasyali and Nora M. El-Gohary

Renewable and Sustainable Energy Reviews, 2018, vol. 81, issue P1, 1192-1205

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.

Keywords: Building energy; Energy consumption prediction; Data-driven methods; Machine learning (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (244)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:81:y:2018:i:p1:p:1192-1205

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DOI: 10.1016/j.rser.2017.04.095

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