Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1530
Título: Strategic bidding methodology for electricity markets using adaptive learning
Autor: Pinto, Tiago
Vale, Zita
Rodrigues, Fátima
Morais, H.
Praça, Isabel
Palavras-chave: Adaptive learning
Electricity markets
Forecasting methods
Intelligent agents
Multiagent systems
Data: 2011
Editora: Springer Berlin Heidelberg
Relatório da Série N.º: Lecture Notes in Computer Science; Vol. 6704
Resumo: The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.
URI: http://hdl.handle.net/10400.22/1530
ISBN: 978-3-642-21826-2
ISSN: 0302-9743
Versão do Editor: http://link.springer.com/chapter/10.1007%2F978-3-642-21827-9_50
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