物联网智能微电网能源交易管理的强化学习新方法

IF 0.5 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

International Journal of Embedded Systems Pub Date : 2023-01-01 DOI:10.1504/ijes.2023.134121

Qiuyu Lu, Haibo Li, Jianping Zheng, Jianru Qin, Yinguo Yang, N.A. Li, Keteng Jiang

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A new reinforcement learning approach for improving energy trading management for smart microgrids in the internet of things

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来源期刊

International Journal of Embedded Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-

CiteScore

2.50

自引率

41.70%

发文量

期刊介绍： With the advent of VLSI system level integration and system-on-chip, the centre of gravity of the computer industry is now moving from personal computing into embedded computing. Embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from vehicles, telephones, audio-video-equipment, aircraft, toys, security systems, medical diagnostics, to weapons, pacemakers, climate control systems, manufacturing systems, intelligent power systems etc. IJES addresses the state of the art of all aspects of embedded computing systems with emphasis on algorithms, systems, models, compilers, architectures, tools, design methodologies, test and applications.