Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5937
Título: Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets
Autor: Pinto, Tiago
Sousa, Tiago
Barreira, Elisa
Praça, Isabel
Vale, Zita
Palavras-chave: Adaptive Learning
Artificial Neural Network
Electricity Markets
Multi-Agent Simulation
Data: Ago-2013
Editora: IEEE
Relatório da Série N.º: DEXA;2013
Resumo: The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.
URI: http://hdl.handle.net/10400.22/5937
DOI: 10.1109/DEXA.2013.49
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6621368&queryText%3DMetalearner+based+on+Dynamic+Neural+Network+for+Strategic+Bidding+in+electricity+Markets
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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