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Advisor(s)
Abstract(s)
This document presents a tool able to
automatically gather data provided by real energy markets and
to generate scenarios, capture and improve market players’
profiles and strategies by using knowledge discovery processes in
databases supported by artificial intelligence techniques, data
mining algorithms and machine learning methods. It provides
the means for generating scenarios with different dimensions and
characteristics, ensuring the representation of real and adapted
markets, and their participating entities. The scenarios generator
module enhances the MASCEM (Multi-Agent Simulator of
Competitive Electricity Markets) simulator, endowing a more
effective tool for decision support. The achievements from the
implementation of the proposed module enables researchers and
electricity markets’ participating entities to analyze data, create
real scenarios and make experiments with them. On the other
hand, applying knowledge discovery techniques to real data also
allows the improvement of MASCEM agents’ profiles and
strategies resulting in a better representation of real market
players’ behavior. This work aims to improve the comprehension
of electricity markets and the interactions among the involved
entities through adequate multi-agent simulation.
Description
Keywords
Electricity Markets Knowledge Discovery in Databases Machine Learning Multi-Agent Simulators Real Electricity Markets Scenarios Generation
Citation
Publisher
IEEE