Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5904
Título: Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support
Autor: Sousa, Tiago
Pinto, Tiago
Praça, Isabel
Vale, Zita
Morais, Hugo
Palavras-chave: Reinforcement Learning
Bayesian theorem
MASCEM
ALBidS
Data: 2014
Editora: Springer
Relatório da Série N.º: Advances in Intelligent Systems and Computing;Vol. 290
Resumo: This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5904
DOI: 10.1007/978-3-319-07593-8_18
Versão do Editor: http://link.springer.com/chapter/10.1007/978-3-319-07593-8_18
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