Electricity markets are becoming more competitive, to some extent due to the increasing number ofplayers that have moved from other sectors to the power industry. This is essentially resulting fromincentives provided to distributed generation. Relevant changes in this domain are still occurring, such asthe extension of national and regional markets to continental scales. Decision support tools have therebybecome essential to help electricity market players in their negotiation process. This paper presentsa metalearner to support electricity market players in bidding definition. The proposed metalearneruses a dynamic artificial neural network to create its own output, taking advantage on several learningalgorithms already implemented in ALBidS (Adaptive Learning strategic Bidding System). The proposedmetalearner considers different weights for each strategy, based on their individual performance. Themetalearner’s performance is analysed in scenarios based on real electricity markets data using MASCEM(Multi-Agent Simulator for Competitive Electricity Markets). Results show that the proposed metalearneris able to provide higher profits to market players when compared to other current methodologies andthat results improve over time, as consequence of its learning process.
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