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Publicações

Publicações por HumanISE

2013

On identifying which intermediate nodes should code in multicast networks

Autores
Pinto, T; Lucani, DE; Médard, M;

Publicação
Proceedings of IEEE International Conference on Communications, ICC 2013, Budapest, Hungary, June 9-13, 2013

Abstract

2013

Metalearner Based on Dynamic Neural Network for Strategic Bidding in Electricity Markets

Autores
Pinto, T; Sousa, TM; Barreira, E; Praça, I; Vale, ZA;

Publicação
24th International Workshop on Database and Expert Systems Applications, DEXA 2013, Prague, Czech Republic, August 26-29, 2013

Abstract

2013

Electricity Markets Portfolio Optimization Using a Particle Swarm Approach

Autores
Guedes, N; Pinto, T; Vale, ZA; Sousa, TM; Sousa, T;

Publicação
24th International Workshop on Database and Expert Systems Applications, DEXA 2013, Prague, Czech Republic, August 26-29, 2013

Abstract

2013

Upper Ontology for Multi-Agent Energy Systems' Applications

Autores
Santos, G; Pinto, T; Vale, ZA; Morais, H; Praça, I;

Publicação
Distributed Computing and Artificial Intelligence - 10th International Conference, DCAI 2013, Salamanca, Spain, May 22-24, 2013

Abstract

2013

Adaptive Learning in Games: Defining Profiles of Competitor Players

Autores
Pinto, T; Vale, ZA;

Publicação
Distributed Computing and Artificial Intelligence - 10th International Conference, DCAI 2013, Salamanca, Spain, May 22-24, 2013

Abstract

2013

Strategic bidding in electricity markets: An agent-based simulator with game theory for scenario analysis

Autores
Pinto, T; Praca, I; Vale, Z; Morais, H; Sousa, TM;

Publicação
INTEGRATED COMPUTER-AIDED ENGINEERING

Abstract
Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players' actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

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