ISEP – GECAD – Comunicações em eventos científicos
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- ABS4GD: a multi-agent system that simulates group decision processes considering emotional and argumentative aspectsPublication . Marreiros, Goreti; Santos, Ricardo; Ramos, Carlos; Neves, José; Bulas-Cruz, JoséEmotion although being an important factor in our every day life it is many times forgotten in the development of systems to be used by persons. In this work we present an architecture for a ubiquitous group decision support system able to support persons in group decision processes. The system considers the emotional factors of the intervenient participants, as well as the argumentation between them. Particular attention will be taken to one of components of this system: the multi-agent simulator, modeling the human participants, considering emotional characteristics, and allowing the exchanges of hypothetic arguments among the participants.
- Adaptable control for electrical generation at irregural wind speedsPublication . Puga, Ricardo; Ferreira, Judite; Cunha, J. Boaventura; Vale, ZitaThe main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.
- Adapting Meeting Tools to Agent DecisionPublication . Barreto, João; Praça, Isabel; Pinto, Tiago; Sousa, Tiago; Vale, ZitaElectricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.
- Adaptive tool for automatic data collection of real electricity marketsPublication . Praça, Isabel; Sousa, Tiago; Freitas, A.; Pinto, Tiago; Vale, Zita; Silva, MarcoThe study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.
- Advances in smart grids - benefits on sharing background experiences from Portugal, Central Europe and BrazilPublication . Vale, Zita; Styczynski, Zbigniew; Hadjsaid, Nouredine; Caire, Raphael; Souza, André; Rolim, Jacqueline; Ferreira, Golberi; Aquiles, José; Morais, H.; Faria, PedroThis paper presents ELECON - Electricity Consumption Analysis to Promote Energy Efficiency Considering Demand Response and Non-technical Losses, an international research project that involves European and Brazilian partners. ELECON focuses on energy efficiency increasing through consumer´s active participation which is a key area for Europe and Brazil cooperation. The project aims at significantly contributing towards the successful implementation of smart grids, focussing on the use of new methods that allow the efficient use of distributed energy resources, namely distributed generation, storage and demand response. ELECON puts together researchers from seven European and Brazilian partners, with consolidated research background and evidencing complementary competences. ELECON involves institutions of 3 European countries (Portugal, Germany, and France) and 4 Brazilian institutions. The complementary background and experience of the European and Brazilian partners is of main relevance to ensure the capacities required to achieve the proposed goals. In fact, the European Union (EU) and Brazil have very different resources and approaches in what concerns this area. Having huge hydro and fossil resources, Brazil has not been putting emphasis on distributed renewable based electricity generation. On the contrary, EU has been doing huge investments in this area, taking into account environmental concerns and also the economic EU external dependence dictated by huge requirements of energy related products imports. Sharing these different backgrounds allows the project team to propose new methodologies able to efficiently address the new challenges of smart grids.
- Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness DetectionPublication . Vitorino, João; Rodrigues, Lourenço; Maia, Eva; Praça, Isabel; Lourenço, AndréDrowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windows and ML models, a feature impact analysis using Shapley Additive Explanations (SHAP), and an adversarial robustness analysis to assess their reliability when processing faulty input data and perturbed HRV signals. The most reliable model was Extreme Gradient Boosting (XGB) and the optimal time window had between 120 and 150 s. Furthermore, the 18 most impactful features were selected and new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.
- Agent-based simulation of electronic marketplaces with decision supportPublication . Praça, Isabel; Viamonte, Maria João; Vale, Zita; Ramos, CarlosThis paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.
- Aggregation of Consumers and Producers in a Community with different Clustering MethodsPublication . Silva, Cátia; Faria, Pedro; Vale, Zita; Starzacher, NikolausThe consumer concept is shaping up as the grid is improving to a smart way. Moving from an actor with little information about what was happening in the energy market, to player with an active and important role in its management. The term prosumer will revolutionize the way the electrical system operates. The possibility of the participation of distributed small-scale energy resources in the network infrastructure changes the current management model. The authors propose a model that optimally associates all concepts. Scheduling, aggregation and compensation are the main phases that compose this model. In this paper, the author focusses only on the second, being the main goal compare between being a consumer, a producer or a prosumer in this method. In this way, two partitional clustering methods were used, testing different k clusters.
- Aggregation of Consumers Participation in the Ramping of a Demand Response EventPublication . Abrishambaf, Omid; Faria, Pedro; Vale, ZitaAs the global population is daily soaring, the need for electrical energy is also increasing. This makes the role of the power distribution network more tangible, as the efficiency of all sectors should be increased. The need for smart management and strategic planning, such as demand response programs are obvious in the context. This paper proposes an aggregator model that employs DR programs for managing the network balance. In this model, a specific analysis has been provided for the ramp period and demand response timeline to show the financial behaviors of the aggregator. In the case study of the paper, two demand response events are proposed using actual consumption profiles and a cost comparison has been presented using various pricing schemes. The results remark the costs related to the ramp period before the event and show how such costs are important in daily electricity expenses of the aggregator model.
- AiD-EM: Adaptive Decision Support for Electricity Markets NegotiationsPublication . Pinto, Tiago; Vale, ZitaThis paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).