A review of data-driven building energy consumption prediction studies. (January 2018)

• Record Type: Journal Article
• Authors: Amasyali, Kadir; El-Gohary, Nora M.
• Is Part Of: Renewable & sustainable energy reviews. Volume 81:Part 1(2018)
• Page Start: 1192
• Page End: 1205
• Publication Date: 2018-01
• Subjects: Building energy -- Energy consumption prediction -- Data-driven methods -- Machine learning; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2017.04.095
• Languages: English
• ISSNs: 1364-0321

Abstract

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.