Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5906
Título: Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines
Autor: Pereira, Rafael
Sousa, Tiago
Pinto, Tiago
Praça, Isabel
Vale, Zita
Morais, Hugo
Palavras-chave: MASCEM
ALBidS
Multi-agent based simulation
Data: 2014
Editora: Springer
Relatório da Série N.º: Advances in Intelligent Systems and Computing;Vol. 293
Resumo: Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5906
DOI: 10.1007/978-3-319-07476-4_2
Versão do Editor: http://link.springer.com/chapter/10.1007/978-3-319-07476-4_2
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