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Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review

Lookup NU author(s): Mohamad Khalil, Dr Stephen McGough, Dr Zoya PourmirzaORCiD, Dr Mehdi Pazhoohesh, Professor Sara Walker


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© 2022 Elsevier Ltd. The building sector accounts for 36 % of the total global energy usage and 40% of associated Carbon Dioxide emissions. Therefore, the forecasting of building energy consumption plays a key role for different building energy management applications (e.g., demand-side management and promoting energy efficiency measures), and implementing intelligent control strategies. Thanks to the advancement of Internet of Things in the last few years, this has led to an increase in the amount of buildings energy related-data. The accessibility of this data has inspired the interest of researchers to utilize different data-driven approaches to forecast building energy consumption. In this study, we first present state of-the-art Machine Learning, Deep Learning and Statistical Analysis models that have been used in the area of forecasting building energy consumption. In addition, we also introduce a comprehensive review of the existing research publications that have been published since 2015. The reviewed literature has been categorized according to the following scopes: (I) building type and location; (II) data components; (III) temporal granularity; (IV) data pre-processing methods; (V) features selection and extraction techniques; (VI) type of approaches; (VII) models used; and (VIII) key performance indicators. Finally, gaps and current challenges with respect to data-driven building energy consumption forecasting have been highlighted, and promising future research directions are also recommended.

Publication metadata

Author(s): Khalil M, McGough AS, Pourmirza Z, Pazhoohesh M, Walker S

Publication type: Note

Publication status: Published

Journal: Engineering Applications of Artificial Intelligence

Year: 2022

Volume: 115

Print publication date: 01/10/2022

Online publication date: 12/08/2022

Acceptance date: 28/07/2022

ISSN (print): 0952-1976

ISSN (electronic): 1873-6769

Publisher: Elsevier Ltd


DOI: 10.1016/j.engappai.2022.105287