Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/7321
Título: Six thinking hats: A novel metalearner for intelligent decision support in electricity markets
Autor: Pinto, Tiago
Barreto, João
Praça, Isabel
Sousa, Tiago M.
Vale, Zita
Solteiro Pires, E.J.
Palavras-chave: Artificial intelligence
Decision support system
Electricity market
Genetic algorithm
Multiagent simulation
Machine learning
Data: Nov-2015
Editora: Elsevier
Relatório da Série N.º: Decision Support Systems;Vol. 79
Resumo: The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.
Peer review: yes
URI: http://hdl.handle.net/10400.22/7321
DOI: 10.1016/j.dss.2015.07.011
ISSN: 0167-9236
Versão do Editor: http://www.sciencedirect.com/science/article/pii/S0167923615001438
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