Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Research on using the decision tree method in order to select the best alternative for the pharmacies supply

Introduction / Aims: Adopting the important decisions represents a specific task of the manager. An efficient manager takes these decisions during a sistematic process with well-defined elements, each with a precise order. In the pharmaceutical practice and business, in the supply process of the pharmacies, there are situations when the medicine distributors offer a certain discount, but require payment in a shorter period of time. In these cases, the analysis of the offer can be made with the help of the decision tree method, which permits identifying the decision offering the best possible result in a given situation. The aims of the research have been the analysis of the product offers of many different suppliers and the establishing of the most advantageous ways of pharmacy supplying. Material / Methods: There have been studied the general product offers of the following medical stores: A&G Med, Farmanord, Farmexim, Mediplus, Montero and Relad. In the case of medicine offers including a discount, the decision tree method has been applied in order to select the most advantageous offers. The Decision Tree is a management method used in taking the right decisions and it is generally used when one needs to evaluate the decisions that involve a series of stages. The tree diagram is used in order to look for the most efficient means to attain a specific goal. The decision trees are the most probabilistic methods, useful when adopting risk taking decisions. Results: The results of the analysis on the tree diagrams have indicated the fact that purchasing medicines with discount (1%, 10%, 15%) and payment in a shorter time interval (120 days) is more profitable than purchasing without a discount and payment in a longer time interval (160 days). Discussion / Conclusion: Depending on the results of the tree diagram analysis, the pharmacies would purchase from the selected suppliers. The research has shown that the decision tree method represents a valuable work instrument in choosing the best ways for supplying pharmacies and it is very useful to the specialists from the pharmaceutical field, pharmaceutical management, to medicine suppliers, pharmacy practitioners from the community pharmacies and especially to pharmacy managers, chief – pharmacists.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

MASCEM - an electricity market simulator providing coalition support for virtual power players

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data.

Ancillary service market simulation

Power systems operation in a liberalized environment requires that market players have access to adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper deals with ancillary services negotiation in electricity markets. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of ancillary services using two different methods (Linear Programming and Genetic Algorithm approaches) is included in the paper.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Personality, emotion and mood simulation in decision making

In this paper is proposed the integration of personality, emotion and mood aspects for a group of participants in a decision-making negotiation process. The aim is to simulate the participant behavior in that scenario. The personality is modeled through the OCEAN five-factor model of personality (Openness, Conscientiousness, Extraversion, Agreeableness and Negative emotionality). The emotion model applied to the participants is the OCC (Ortony, Clore and Collins) that defines several criteria representing the human emotional structure. In order to integrate personality and emotion is used the pleasure-arousal-dominance (PAD) model of mood.

Personality, emotion, and mood in agent-based group decision making

Group decision making plays an important role in organizations, especially in the present-day economy that demands high-quality, yet quick decisions. Group decision-support systems (GDSSs) are interactive computer-based environments that support concerted, coordinated team efforts toward the completion of joint tasks. The need for collaborative work in organizations has led to the development of a set of general collaborative computer-supported technologies and specific GDSSs that support distributed groups (in time and space) in various domains. However, each person is unique and has different reactions to various arguments. Many times a disagreement arises because of the way we began arguing, not because of the content itself. Nevertheless, emotion, mood, and personality factors have not yet been addressed in GDSSs, despite how strongly they influence results. Our group’s previous work considered the roles that emotion and mood play in decision making. In this article, we reformulate these factors and include personality as well. Thus, this work incorporates personality, emotion, and mood in the negotiation process of an argumentbased group decision-making process. Our main goal in this work is to improve the negotiation process through argumentation using the affective characteristics of the involved participants. Each participant agent represents a group decision member. This representation lets us simulate people with different personalities. The discussion process between group members (agents) is made through the exchange of persuasive arguments. Although our multiagent architecture model4 includes two types of agents—the facilitator and the participant— this article focuses on the emotional, personality, and argumentation components of the participant agent.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

Estratégias de oferta em leilões simétricos no âmbito dos mercados liberalizados de electricidade

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

A similarity measure for music signals

One of the goals in the field of Music Information Retrieval is to obtain a measure of similarity between two musical recordings. Such a measure is at the core of automatic classification, query, and retrieval systems, which have become a necessity due to the ever increasing availability and size of musical databases. This paper proposes a method for calculating a similarity distance between two music signals. The method extracts a set of features from the audio recordings, models the features, and determines the distance between models. While further work is needed, preliminary results show that the proposed method has the potential to be used as a similarity measure for musical signals.

Satisfação de agentes afectivos no processo de tomada de decisão em grupo

Um dos factores mais determinantes para o sucesso de uma organização é a qualidade das decisões tomadas. Para que as decisões tomadas sejam melhores e potenciem a competitividade das organizações, sistemas como os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) têm sido fortemente desenvolvidos e estudados nas últimas décadas. Cada vez mais, estes sistemas são populados com um maior número de dados, com o objectivo de serem mais assertivos. Considera-se que com determinados dados seja possível que o sistema possa aferir a satisfação dos participantes com as decisões tomadas, tendencialmente de forma automática. Hoje em dia, as análises de satisfação com as decisões não contemplam na sua maioria factores imprescindíveis, como os emocionais e de personalidade, sendo que os modelos existentes tendem a ser incompletos. Nesta dissertação propõe-se uma metodologia que permite a um SADG aferir a satisfação do participante com a decisão, considerando aspectos como a personalidade, as emoções e as expectativas. A metodologia desenvolvida foi implementada num SADG (ArgEmotionsAgents) com uma arquitectura multiagente, composto por agentes que reflectem participantes reais e que estão modelados com a sua personalidade. De acordo com a sua personalidade, esses agentes trocam argumentos persuasivos de forma a obterem uma decisão consensual. No processo de troca de argumentos os agentes geram emoções que afectam o seu humor. A implementação da metodologia no ArgEmotionsAgents permite que, no final de uma reunião, seja possível aferir a satisfação dos agentes participantes com a decisão final e com o processo que levou à tomada de decisão. De forma a validar a metodologia proposta bem como a implementação que foi desenvolvida, foram realizadas quatro experiências em diferentes cenários. Numa primeira experiência, foi aferida a satisfação dos quatro agentes participantes. Nas experiências seguintes, um dos agentes participantes foi usado como referência e foram alteradas configurações (expectativas, personalidade e reavaliação das alternativas) para perceber de que forma os vários factores afectam a satisfação. Com o estudo concluiu-se que todos os factores considerados no modelo afectam a satisfação. A forma como a satisfação é afectada por cada um dos factores vai ao encontro da lógica apresentada no estado da arte. Os resultados de satisfação aferidos pelo modelo são congruentes.

Sistema de apoio à geração de ideias em grupo considerando as emoções e os aspectos sociais

As emoções dos indivíduos e o contexto social do grupo onde estes estão inseridos têm influência no seu desempenho no que se refere ao desenvolvimento de várias tarefas, incluindo as que são realizadas via electrónica. O processo de geração de ideias em grupo mediado por computador tem vantagens consideráveis em relação ao processo de geração de ideias em grupo tradicional, nomeadamente no que se refere ao aumento da sinergia entre os elementos do grupo, à existência da memória de grupo e à possibilidade dos elementos estarem dispersos no espaço e no tempo. Com isto em mente, o presente trabalho pretende analisar a importância do estado de espírito do participante e a influência que os vários aspectos sociais têm no participante, para assim ser possível tomar determinadas acções com o objectivo de potenciar o desempenho dos utilizadores ao longo da reunião de geração de ideias. Neste trabalho é analisada a influência que o estado de espírito dos participantes e o contexto social das reuniões podem ter no sucesso de uma reunião de geração de ideias electrónica. Considerando a influência de estes factores, é proposto um modelo que inclui essas variáveis no processo de geração de ideias em grupo mediado por computador. Com isto pretende-se demonstrar que a inclusão do modelo proposto numa ferramenta de apoio à geração de ideias em grupo permite melhorar o desempenho individual e consequentemente o desempenho do grupo, bem como a interacção entre todos os elementos. Assim, este trabalho pretende gerar sugestões com o objectivo de manter os participantes atentos e motivados para as tarefas que têm de realizar, nomeadamente a tarefa de geração de ideias. Com o objectivo de aplicar o modelo proposto é também apresentado neste trabalho uma nova ferramenta de geração de ideias em computador que considera o contexto emocional e social da reunião, o S-IGTAI (Social Idea Generation Tool for Ambient Intelligence). Através das interacções entre os participantes e a ferramenta S-IGTAI, é recolhida informação que será o input do modelo proposto, sendo que o output serão as sugestões enviadas para o facilitador. Estas sugestões têm o propósito que o facilitador realize recomendações aos participantes no sentido de manter os seus estados de espírito num nível positivo e eliminar a influência negativa dos vários aspectos sociais, potenciando dessa forma o desempenho de todos os participantes. Com a finalidade de validar o modelo proposto e a nova ferramenta (S-IGTAI) é apresentado um caso de estudo neste documento que permite realizar a avaliação do trabalho desenvolvido.