Consensus Algorithms and Deep Reinforcement Learning in Energy Market: A Review

JOGUNOLA, Olamide, ADEBISI, Bamidele, IKPEHAI, Augustine, POPOOLA, Segun, GUI, Guan, GACANIN, Haris and CI, Song (2020). Consensus Algorithms and Deep Reinforcement Learning in Energy Market: A Review. IEEE Internet of Things Journal.

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Abstract

Blockchain (BC) and artificial intelligence (AI) are often utilised separately in energy trading systems (ETS). However, these technologies can complement each other and reinforce their capabilities when integrated. This paper provides a comprehensive review of consensus algorithms (CA) of BC and deep reinforcement learning (DRL) in ETS. While the distributed consensus underpins the immutability of transaction records of prosumers, the deluge of data generated paves the way to use AI algorithms for forecasting and address other data analytic related issues. Hence, the motivation to combine BC with AI to realise secure and intelligent ETS. This study explores the principles, potentials, models, active research efforts and unresolved challenges in the CA and DRL. The review shows that despite the current interest in each of these technologies, little effort has been made at jointly exploiting them in ETS due to some open issues. Therefore, new insights are actively required to harness the full potentials of CA and DRL in ETS. We propose a framework and offer some perspectives on effective BC-AI integration in ETS.

Item Type:	Article
Additional Information:	© 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords:	0805 Distributed Computing; 1005 Communications Technologies
Identification Number:	https://doi.org/10.1109/JIOT.2020.3032162
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	22 Oct 2020 11:57
Last Modified:	17 Mar 2021 21:31
URI:	https://shura.shu.ac.uk/id/eprint/27490

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