Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types

Ayas Shaqour and Aya Hagishima ()
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Ayas Shaqour: Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga City 816-8580, Japan
Aya Hagishima: Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga City 816-8580, Japan

Energies, 2022, vol. 15, issue 22, 1-27

Abstract: Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations.

Keywords: building energy demand; deep reinforcement learning; data-driven control; energy demand prediction; energy efficiency; energy management; residential building; office building; commercial building; data centre (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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