Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1308
Título: Intelligent decision making in electricity markets: simulated annealing Q-Learning
Autor: Pinto, Tiago
Sousa, Tiago
Vale, Zita
Morais, H.
Praça, Isabel
Palavras-chave: Adaptive learning
Electricity markets
Multiagent simulation
Reinforcement learning
Simulated annealing
Data: 2012
Editora: IEEE
Resumo: Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.
URI: http://hdl.handle.net/10400.22/1308
ISBN: 978-1-4673-2728-2
ISSN: 1944-9925
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6345606
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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