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Browsing ISEP – GECAD – Artigos by Subject "Adaptive learning"
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- Adaptive learning in agents behaviour: A framework for electricity markets simulationPublication . Pinto, Tiago; Vale, Zita; Sousa, Tiago; Praça, Isabel; Santos, Gabriel; Morais, HugoElectricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. 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. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.
- Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data ContextPublication . Peixoto, Rafael; Cruz, Christophe; Silva, NunoOne of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documents. However, data is always being generated and its statistical properties can change over time. In order to learn in such environment, the classification processes must handle streams of non-stationary data to adapt the classification model. This paper proposes a new adaptive learning process to consistently adapt the ontologydescribed classification model according to a non-stationary stream of unstructured text data in Big Data context. The adaptive process is then instantiated for the specific case of of the previously proposed Semantic HMC.
- Adaptive Portfolio Optimization for Multiple Electricity Markets ParticipationPublication . Pinto, Tiago; Morais, Hugo; Sousa, Tiago M.; Sousa, Tiago; Vale, Zita; Praça, Isabel; Faia, Ricardo; Pires, Eduardo José SolteiroThe increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources’ particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players’ participation in electric power negotiations while improving energy efficiency. The opportunity for players’ participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator—MIBEL.
- Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive LearningPublication . Pinto, Tiago; Vale, Zita; Praça, Isabel; Pires, E.; Lopes, FernandoThis paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.
- Metalearning to support competitive electricity market players’strategic biddingPublication . Pinto, Tiago; Sousa, Tiago M.; Morais, Hugo; Praça, Isabel; Vale, ZitaElectricity markets are becoming more competitive, to some extent due to the increasing number ofplayers that have moved from other sectors to the power industry. This is essentially resulting fromincentives provided to distributed generation. Relevant changes in this domain are still occurring, such asthe extension of national and regional markets to continental scales. Decision support tools have therebybecome essential to help electricity market players in their negotiation process. This paper presentsa metalearner to support electricity market players in bidding definition. The proposed metalearneruses a dynamic artificial neural network to create its own output, taking advantage on several learningalgorithms already implemented in ALBidS (Adaptive Learning strategic Bidding System). The proposedmetalearner considers different weights for each strategy, based on their individual performance. Themetalearner’s performance is analysed in scenarios based on real electricity markets data using MASCEM(Multi-Agent Simulator for Competitive Electricity Markets). Results show that the proposed metalearneris able to provide higher profits to market players when compared to other current methodologies andthat results improve over time, as consequence of its learning process.
- Negotiation context analysis in electricity marketsPublication . Pinto, Tiago; Vale, Zita; Sousa, Tiago M.; Praça, IsabelContextualization is critical in every decision making process. Adequate responses to problems depend not only on the variables with direct influence on the outcomes, but also on a correct contextualization of the problem regarding the surrounding environment. Electricity markets are dynamic environments with increasing complexity, potentiated by the last decades' restructuring process. Dealing with the growing complexity and competitiveness in this sector brought the need for using decision support tools. A solid example is MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), whose players' decisions are supported by another multiagent system – ALBidS (Adaptive Learning strategic Bidding System). ALBidS uses artificial intelligence techniques to endow market players with adaptive learning capabilities that allow them to achieve the best possible results in market negotiations. This paper studies the influence of context awareness in the decision making process of agents acting in electricity markets. A context analysis mechanism is proposed, considering important characteristics of each negotiation period, so that negotiating agents can adapt their acting strategies to different contexts. The main conclusion is that context-dependant responses improve the decision making process. Suiting actions to different contexts allows adapting the behaviour of negotiating entities to different circumstances, resulting in profitable outcomes.