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


    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
    Publication date2014
    ISBN (Print)978-3-319-07475-7
    ISBN (Electronic)978-3-319-07476-4
    Publication statusPublished - 2014
    SeriesAdvances in Intelligent Systems and Computing


    Dive into the research topics of 'Strategic Bidding for Electri city Markets Negotiation Using Support Vector Machines'. Together they form a unique fingerprint.

    Cite this