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Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Adaptive Learning Artificial Neural Network Electricity Markets Multi-Agent Simulation Metalearning
Contexto Educativo
Citação
Editora
IEEE