Home // ICAS 2014, The Tenth International Conference on Autonomic and Autonomous Systems // View article

Operation of Accumulator-Bank Serving Agent System Using Machine Learning

Authors:
Ágnes Werner-Stark
Tibor Dulai
Katalin M. Hangos

Keywords: renewable energy; agent; genetic algorithm (GA); cooperation

Abstract:
Advancements in on-demand power management of renewable energy can be achieved by multi-agent systems. This paper proposes an innovative approach where a population of autonomous agents are able to cooperate in managing an accumulator-bank in order to effectively deliver energy in places where it is required. The distributed and adaptive multi-agent approach is able to decrease the interferences by avoiding the negative interactions and conflicts, using the cooperation among agents. Our method uses the learning ability of agents to minimize the number of communications among agents and the central unit. This adaptive behavior lets the agents minimize the time to find the optimal routes during the search. A simulation environment has also been developed for visualizing the movements of the agents and the conflict situations. The operation and the efficiency of the algorithm have been investigated using simple case studies.

Pages: 25 to 30

Copyright: Copyright (c) IARIA, 2014

Publication date: April 20, 2014

Published in: conference

ISSN: 2308-3913

ISBN: 978-1-61208-331-5

Location: Chamonix, France

Dates: from April 20, 2014 to April 24, 2014