874 resultados para decision support systems, GIS, interpolation, multiple regression
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Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.
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Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).
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The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.
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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.
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Electricity market players operating in a liberalized environment require adequate decision support tools, 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. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.
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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.
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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 tool 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 based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.
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This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.
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Shopping centers present a rich and heterogeneous environment, where IT systems can be implemented in order to support the needs of its actors. However, due to the environment complexity, several feasibility issues emerge when designing both the logical and physical architecture of such systems. Additionally, the system must be able to cope with the individual needs of each actor, and provide services that are easily adopted by them, taking into account several sociological and economical aspects. In this sense, we present an overview of current support systems for shopping center environments. From this overview, a high-level model of the domain (involving actors and services) is described along with challenges and possible features in the context of current Semantic Web, mobile device and sensor technologies.
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Copyright 2013 Springer Netherlands.
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Mestrado em Engenharia Informática
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Mestrado em Engenharia Informática - Área de Especialização em Arquitecturas, Sistemas e Redes
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Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo de cooperação entre o ISEL e o LNEC
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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.
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O contributo da área de investigação Interacção Humano-Computador (HCI) está patente não só na qualidade da interacção, mas também na diversificação das formas de interacção. A HCI define-se como sendo uma disciplina que se dedica ao desenho, desenvolvimento e implementação de sistemas de computação interactivos para uso humano e estudo dos fenómenos relevantes que os rodeiam. Pretende-se, no âmbito desta tese de mestrado, o desenvolvimento de um Editor Gráfico de Layout Fabril a integrar num SAD para suporte ao Planeamento e Controlo da Produção. O sistema deve ser capaz de gerar um layout fabril do qual constam, entre outros objectos, as representações gráficas e as respectivas características/atributos do conjunto de recursos (máquinas/processadores) existentes no sistema de produção a modelar. O módulo desenvolvido será integrado no projecto de I&D ADSyS (Adaptative Decision Support System for Interactive Scheduling with MetaCognition and User Modeling Experience), melhorando aspectos de interacção referentes ao sistema AutoDynAgents, um dedicado ao escalonamento, planeamento e controlo de produção. Foi realizada a análise de usabilidade a este módulo com a qual se pretendeu realizar a respectiva avaliação, através da realização de um teste de eficiência e do preenchimento de um inquérito, da qual se identificaram um conjunto de melhorias e sugestões a serem consideradas no refinamento deste módulo.