Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree-Reference-Cited by-同舟云学术

Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree

Published:2020-02-10 Issue:4 Volume:13 Page:479-494
ISSN:1872-4981
Container-title:Intelligent Decision Technologies
language:
Short-container-title:IDT

Author:

EL Bourakadi Dounia,Yahyaouy Ali,Boumhidi Jaouad

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Reference38 articles.

1. Lasseter RH, Paigi P. Microgrid: a conceptual solution. Proceedings of the IEEE 35th; Annual Power Electronics Specialists Conference (PESC 04), 2004; 4285-4290.

2. Power management of hybridmicro-grid system by a generic centralized supervisory control scheme;Ambia;Sustain Energy, Technol Assess.,2014

3. Multi-agent systems for power engineering applications – Part II: Technologies, standards, and tools for building multi-agent systems;McArthur;IEEE Transactions on Power Systems.,2007

4. Siljak DD. Decentralized control of complex systems. Dover Publications, 2012.

5. A New approach for multi-agent coalition formation and management in the scope of electricity markets;Pinto;Energy.,2011

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