Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1428
Título: Metalearning in ALBidS: A Strategic Bidding System for electricity markets
Autor: Pinto, Tiago
Sousa, Tiago
Vale, Zita
Praça, Isabel
Morais, H.
Palavras-chave: Adaptive learning
Electricity markets
Intelligent agents
Data: 2012
Editora: Springer Berlin Heidelberg
Relatório da Série N.º: Advances in intelligent and soft computing; Vol. 156
Resumo: Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.
URI: http://hdl.handle.net/10400.22/1428
ISBN: 978-3-642-28761-9
ISSN: 1867-5662
Versão do Editor: http://link.springer.com/chapter/10.1007/978-3-642-28762-6_30
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