959 resultados para semi-parametric approach.


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The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

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Although we have many electric devices at home, there are just few systems to evaluate, monitor and control them. Sometimes users go out and leave their electric devices turned on what can cause energy wasting and dangerous situations. Therefore most of the users may want to know the using states of their electrical appliances through their mobile devices in a pervasive way. In this paper, we propose an Intelligent Supervisory Control System to evaluate, monitor and control the use of electric devices in home, from outside. Because of the transferring data to evaluate, monitor and control user's location and state of home (ex. nobody at home) may be opened to attacks leading to dangerous situations. In our model we include a location privacy module and encryption module to provide security to user location and data. Intelligent Supervising Control System gives to the user the ability to manage electricity loads by means of a multi-agent system involving evaluation, monitoring, control and energy resource agents.

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This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

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The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

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Este estudo pretende descrever e compreender o processo formativo dos professores do 1º ciclo do Ensino Básico, no contexto do Programa Nacional de Ensino do Português (PNEP), as suas dimensões mais relevantes e como é percecionada a supervisão nesse processo de formação contínua. A metodologia adotada nesta investigação foi de natureza qualitativa e baseou-se num conjunto de entrevistas semi-diretivas realizadas a formandos que frequentaram o PNEP entre 2008/2010. Dos resultados obtidos foi possível verificar o impacto positivo que esta modalidade de formação contínua teve no desenvolvimento profissional dos professores preparando-os para a implementação do novo programa de português e permitindo-lhes desmistificar o papel do supervisor através da participação numa experiência de supervisão. O trabalho colaborativo entre professores emergiu durante este processo formativo fomentando a construção de saber em colaboração. A prática pedagógica dos professores apresentou mudanças que pretenderam repercutir-se no aproveitamento dos alunos na área da língua portuguesa. - Abstract This study aims to comprehend and describe the formative process and practice of teachers in Basic education (1st Cycle), in the context of the National Programme for the Teaching of Portuguese Language (PNEP), its most relevant dimensions, the factors which affected it and how supervision was perceived by the teachers involved. A qualitative approach was used, based on semi-directed interviews done to teachers that attended the PNEP, from 2008 to 2010. The impact of this type of continuing training on teachers‟ professional development was positive. They felt prepared for the implementation of the new Portuguese Language Programme. They also demystified the role of the supervisor through the participation in an experience of supervision. The teachers‟ collaborative work emerged during this formative process, promoting the construction of knowledge among teachers. Added to this, teachers‟ pedagogical practice also improved reflected on the students‟ proficiency in the Portuguese language.

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Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must be completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.

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Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação, especialização Supervisão em Educação

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Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação, especialidade em Educação Artística -Teatro na Educação

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This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

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In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. This paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. The use of DemSi by a retailer in a situation of energy shortage, is presented. Load reduction is obtained using a consumer based price elasticity approach supported by real time pricing. Non-linear programming is used to maximize the retailer’s profit, determining the optimal solution for each envisaged load reduction. The solution determines the price variations considering two different approaches, price variations determined for each individual consumer or for each consumer type, allowing to prove that the approach used does not significantly influence the retailer’s profit. The paper presents a case study in a 33 bus distribution network with 5 distinct consumer types. The obtained results and conclusions show the adequacy of the used methodology and its importance for supporting retailers’ decision making.

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Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação - Especialidade Supervisão em Educação

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Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

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Energy Resources Management can play a very relevant role in future power systems in SmartGrid context, with high penetration of distributed generation and storage systems. This paper deals with the importance of resources management in incident situation. The system to consider a high penetration of distributed generation, demand response, storage units and network reconfiguration. A case study evidences the advantages of using a flexible SCADA to control the energy resources in incident situation.

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Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.

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Introdução: No Centro Hospitalar de São João, EPE a partir de 2008, foi implementado o sistema semi-automático de reposição de stocks nivelados de medicamentos, Pyxis®, atualmente implementado em 16 serviços clínicos. Atendendo ao crescimento verificado na implementação deste sistema automatizado na instituição, este trabalho pretende dar a conhecer a realidade de preparação da medicação para reposição no sistema semi-automático Pyxis®, por avaliação do número de unidades de medicamentos repostos diariamente e por dia da semana. Material e Métodos: Desenvolveu-se um estudo longitudinal retrospetivo onde se analisou a totalidade de serviços com implementação Pyxis® através do registo diário de reposição dos diferentes Serviços Clínicos num período de 41 dias consecutivos. Numa segunda fase, os dados foram sintetizados sob a forma de tabelas em Microsoft Office Excel®, tendo posteriormente sido construídos os respetivos gráficos para análise. Resultados: Os resultados, representados graficamente, mostram que a segunda-feira é o dia da semana com maior número de reposições de medicamentos, sendo os serviços com maior número de reposições totais UCI Geral, UCI Neurocríticos, Cirurgia Cardiotorácica e UCIPU. Discussão / Conclusões: Os resultados obtidos permitiram verificar uma sobrecarga de referências de medicamentos e unidades repostas às segundas-feiras, atingindo, em muitos serviços, valores de unidades repostas duas vezes superior à média de reposições do serviço (por ex. UCI Neurocíticos). Contudo, apesar do reduzido período de análise, os dados parecem evidenciar que o facto de haver reposições ao domingo agiliza o processo de reposição dos Pyxis® às segundas-feiras.