Strategic Bidding for Electri city Markets Negotiation Using Support Vector Machines

Rafael Pereira, Tiago Sousa, Tiago Pinto, Isabel Praca, Hugo Morais

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

nergy 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
Original languageEnglish
Title of host publicationTrends in Practical Applications of Heterogeneous Multi-Agent Systems. : The PAAMS Collection
EditorsJ.M. Corchado
PublisherSpringer
Publication date2014
Pages9-17
ISBN (Print)978-3-319-07475-7
ISBN (Electronic)978-3-319-07476-4
DOIs
Publication statusPublished - 2014
SeriesAdvances in Intelligent Systems and Computing
Volume293
ISSN2194-5357

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