Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5962
Título: Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets
Autor: Teixeira, Brígida
Silva, Francisco
Pinto, Tiago
Praça, Isabel
Santos, Gabriel
Vale, Zita
Palavras-chave: Data-Mining
Electricity Markets
Knowledge Discovery in Databases
Machine Learning
Multi-Agent Simulation
Scenarios Generation
Data: Dez-2014
Editora: IEEE
Relatório da Série N.º: IA;2014
Resumo: This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.
URI: http://hdl.handle.net/10400.22/5962
DOI: 10.1109/IA.2014.7009452
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7009452&queryText%3DData+Mining+Approach+to+support+the+Generation+of+Realistic+Scenarios+for+Multi-Agent+simulation+of+Electricity+Markets
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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