Estudo da importância do transporte ferroviário de mercadorias no Eixo Leixões- Salamanca

A evolução dos transportes de mercadorias, em Portugal e na União Europeia, assume uma enorme repercussão na economia global, de modo que a combinação dos vários modos de transporte, com vista à obtenção de ganhos ao nível da eficiência, adquire extrema importância. Os programas europeus de apoio aos transportes, enquadrados na rede transeuropeia de transportes e plataformas logísticas, também contribuem para a otimização do transporte de mercadorias. A importância do transporte ferroviário de mercadorias no eixo Leixões- Salamanca, nomeadamente para as empresas exportadoras e importadoras da região norte e centro, que utilizam meios alternativos ao ferroviário, constitui o principal objetivo desta dissertação. A revisão bibliográfica inclui uma abordagem aos transportes de mercadorias em geral e de forma mais aprofundada aos modos ferroviário e rodoviário na península ibérica, passando pela logística, bem como pela integração de modos: intermodalidade e multimodalidade na rede europeia de transportes e ainda a referência aos portos secos e às plataformas logísticas. Isto permite caraterizar as diferentes empresas operadoras do setor dos transportes de mercadorias e os produtos transacionados, assim como enumerar vantagens e/ou desvantagens do meio de transporte ferroviário face a outros meios, mais concretamente no eixo alvo deste estudo. A metodologia utilizada consiste na análise de informação proveniente de fontes secundárias havendo lugar a uma referência mais detalhada sobre as plataformas logísticas de Leixões e Salamanca, o eixo E-80, o corredor ferroviário nº4 e os programas europeus promotores da eficiência no transporte ferroviário de mercadorias: Marathon, Ferremed e Marco Polo. Para a recolha de informação primária o instrumento adotado foi a entrevista semiestruturada, efetuada a dois representantes da empresa CP-Carga e a um representante da empresa KLog, ambas as empresas ligadas ao setor dos transportes e logística. A análise e tratamento de toda a informação recolhida possibilitam, desde logo, evidenciar as potencialidades do eixo Leixões-Salamanca no que se refere ao transporte ferroviário de mercadorias, delinear recomendações para a sua otimização, bem como efetuar uma análise SWOT. As considerações finais revelam que é imperativo adotar medidas, de forma integrada, para que o seu efeito na potenciação do transporte ferroviário de mercadorias, não só no eixo Leixões-Salamanca, mas também a nível europeu, se afirme como uma verdadeira alternativa a outros modos, particularmente ao domínio rodoviário.

A comunicação on-line das ONGs e a angariação de fundos

Dissertação de Mestrado apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação da Mestre Inês Veiga Pereira “Esta versão contém as críticas e sugestões dos elementos do júri”

A comparative study between centralization and decentralization of diagnostic reasoning in EPS

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa de Lisboa para obtenção do grau de mestre em Engenharia Electrotécnica e de Computadores

Georeferencing Road Accidents with Google Earth: Transforming Information into Knowledge for Decision Support

Over the last fifty years mobility practices have changed dramatically, improving the way travel takes place, the time it takes but also on matters like road safety and prevention. High mortality caused by high accident levels has reached untenable levels. But the research into road mortality stayed limited to comparative statistical exercises which go no further than defining accident types. In terms of sharing information and mapping accidents, little progress has been mad, aside from the normal publication of figures, either through simplistic tables or web pages. With considerable technological advances on geographical information technologies, research and development stayed rather static with only a few good examples on dynamic mapping. The use of Global Positioning System (GPS) devices as normal equipments on automobile industry resulted in a more dynamic mobility patterns but also with higher degrees of uncertainty on road traffic. This paper describes a road accident georeferencing project for the Lisbon District involving fatalities and serious injuries during 2007. In the initial phase, individual information summaries were compiled giving information on accidents and its majour characteristics, collected by the security forces: the Public Safety Police Force (Polícia de Segurança Pública - PSP) and the National Guard (Guarda Nacional Republicana - GNR). The Google Earth platform was used to georeference the information in order to inform the public and the authorities of the accident locations, the nature of the location, and the causes and consequences of the accidents. This paper also gives future insights about augmented reality technologies, considered crucial to advances to road safety and prevention studies. At the end, this exercise could be considered a success because of numerous consequences, as for stakeholders who decide what to do but also for the public awareness to the problem of road mortality.

Gestão Inteligente de uma Habitação considerando o Controlo de Cargas, de Microgeração e Veículos Elétricos em Função do seu Perfil de Utilização

Os consumidores finais são vistos, no novo paradigma da operação das redes elétricas, como intervenientes ativos com capacidade para gerir os seus recursos energéticos, nomeadamente as cargas, as unidades de produção, os veículos elétricos e a participação em eventos de Demand Response. Tem sido evidente um aumento do consumo de energia, sendo que o setor residencial representa uma importante parte do consumo global dos países desenvolvidos. Para que a participação ativa dos consumidores seja possível, várias abordagens têm vindo a ser propostas, com ênfase nas Smart Grids e nas Microgrids. Diversos sistemas têm sido propostos e desenvolvidos com o intuito de tornar a operação dos sistemas elétricos mais flexível. Neste contexto, os sistemas de gestão de instalações domésticas apresentam-se como um elemento fulcral para a participação ativa dos consumidores na gestão energética, permitindo aos operadores de sistema coordenarem a produção mas também a procura. No entanto, é importante identificar as vantagens da implementação e uso de sistemas de gestão de energia elétrica para os consumidores finais. Nesta dissertação são propostas metodologias de apoio ao consumidor doméstico na gestão dos recursos energéticos existentes e a implementação das mesmas na plataforma de simulação de um sistema de gestão de energia desenvolvido para consumidores domésticos, o SCADA House Intelligent Management (SHIM). Para tal, foi desenvolvida uma interface que permite a simulação em laboratório do sistema de gestão desenvolvido. Adicionalmente, o SHIM foi incluído no simulador Multi-Agent Smart Grid Simulation Plataform (MASGriP) permitindo a simulação de cenários considerando diferentes agentes. Ao nível das metodologias desenvolvidas são propostos diferentes algoritmos de gestão dos recursos energéticos existentes numa habitação, considerando utilizadores com diferentes tipos de recursos (cargas; cargas e veículos elétricos; cargas, veículos elétricos e microgeração). Adicionalmente é proposto um método de gestão dinâmica das cargas para eventos de Demand Response de longa duração, considerando as características técnicas dos equipamentos. Nesta dissertação são apresentados cinco casos de estudos em que cada um deles tem diferentes cenários de simulação. Estes casos de estudos são importantes para verificar a viabilidade da implementação das metodologias propostas para o SHIM. Adicionalmente são apresentados na dissertação perfis reais dos vários recursos energéticos e de consumidores domésticos que são, posteriormente, utilizados para o desenvolvimento dos casos de estudo e aplicação das metodologias.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Empreitada de construção dos armazéns do polo 2 da plataforma logística do porto de Leixões - Direção e controlo da produção

A Plataforma Logística do Porto de Leixões, administrada pela Administração dos Portos do Douro e Leixões, S.A. (APDL), integra dois Polos situados no concelho de Matosinhos em locais estratégicos para o desenvolvimento das atividades portuária e de logística. É neste contexto que a empresa Luís Simões contactou a APDL no sentido de alugar um espaço para se instalar no Polo 2 da Plataforma Logística do Porto de Leixões. Para que este contrato fosse celebrado existiu um compromisso da APDL de construir dois armazéns com cerca de 10.000m2 cada e ainda um edifício administrativo com cerca de 2.900m2 e todas as redes de infraestruturas, circulações e arranjos exteriores. Após a realização de Concurso Público, a Empreitada de Construção, foi adjudicada à empresa DST - Domingos da Silva Teixeira, S.A.. O presente relatório é referente a um estágio realizado na DST, S.A., em obra, no período de 31 de Janeiro de 2014 e 31 de Julho de 2014. O estágio englobou a direção e controlo da produção das atividades de construção civil que decorreram na empreitada durante este período. O estágio foi efetuado em ambiente real de obra tendo seguido o planeamento habitual de uma empreitada. Foram desenvolvidas numa primeira fase as atividades de preparação e lançamento de consultas de subempreitadas. De seguida foram desenvolvidas tarefas de preparação, controlo de fornecimento, apoio e acompanhamento dos subempreiteiros em obra, destacando-se o acompanhamento dos trabalhos de revestimento exteriores dos edifícios e dos pavimentos de alta planimetria.

Virtual Power Player Using Demand Response to Deal with Unexpected Low Wind Power Generation

Demand response is assumed as an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets and of the increasing use of renewable-based energy sources. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed in this paper aims the minimization of the operation costs in a distribution network operated by a virtual power player that manages the available energy resources focusing on hour ahead re-scheduling. When facing lower wind power generation than expected from day ahead forecast, demand response is used in order to minimize the impacts of such wind availability change. In this way, consumers actively participate in regulation up and spinning reserve ancillary services through demand response programs. Real time pricing is also applied. The proposed model is especially useful when actual and day ahead wind forecast differ significantly. Its application is illustrated in this paper implementing the characteristics of a real resources conditions scenario in a 33 bus distribution network with 32 consumers and 66 distributed generators.

Public works policy in Portugal: a case study in unsustainability

International Journal of Engineering and Industrial Management, nº 1, p. 195-208

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

Intelligent decision support systems for collaboration in industrial plants

Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia