期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 6, 页码 4211-4227出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3032162
关键词
Distributed ledger; Consensus algorithm; Security; Reinforcement learning; Peer-to-peer computing; Support vector machines; Artificial intelligence (AI); blockchain (BC); consensus algorithm (CA); deep reinforcement learning (DRL); distributed ledger technology (DLT); energy market; Markov decision process (MDP)
资金
- ENERGY-IQ, a U.K.-Canada Power Forward Smart Grid Demonstrator Project - Department for Business, Energy and Industrial Strategy [7454460]
- National Key Research and Development Program of China [2018YFC1902202]
This article discusses the application of blockchain and artificial intelligence in energy trading systems, arguing that they can complement each other and enhance system efficiency. Despite current interest in these technologies, new insights are needed to fully exploit their potential due to unresolved issues.
Blockchain (BC) and artificial intelligence (AI) are often utilized separately in energy trading systems (ETSs). However, these technologies can complement each other and reinforce their capabilities when integrated. This article provides a comprehensive review of consensus algorithms (CAs) 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 realize 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.
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