811 resultados para crowdfunding,equity-based crowdfunding,financial forecasting


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Orientador: Doutor, José Manuel Veiga Pereira

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Dissertação para a obtenção do Grau de Mestre em Contabilidade e Finanças Orientador: Mestre Adalmiro Álvaro Malheiro de Castro Andrade Pereira

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Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

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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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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.

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This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

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Este trabalho é uma análise dos efeitos da implementação das últimas recomendações do Basel Committee on Banking Supervision (BCBS) também conhecidas como o Basel III de 2010 que deverão ser faseadamente implementadas desde 1 de Janeiro de 2013 até 1 de Janeiro de 2019, no capital próprio dos bancos Portugueses. Neste trabalho assume-se que os ativos pesados pelo risco de 2012 mantêm-se constantes e o capital terá de ser aumentado segundo as recomendações ano após ano até ao fim de 2018. Com esta análise, pretende-se entender o nível de robustez do capital próprio dos bancos Portugueses e se os mesmos têm capital e reservas suficientes para satisfazer as recomendações de capital mínimo sugeridas pelo BCBS ou caso contrário, se necessitarão de novas injeções de capital ou terão de reduzir a sua atividade económica. O Basel III ainda não foi implementado em Portugal, pois a União Europeia está no processo de desenvolvimento e implementação do Credit Requirement Directive IV (CRD IV) que é uma recomendação que todos os bancos centrais dos países da zona Euro deverão impor aos respetivos bancos. Esta diretiva da União Europeia é baseada totalmente nas recomendações do Basel III e deverá ser implementada em 2014 ou nos anos seguintes. Até agora, os bancos Portugueses seguem um sistema com base no aviso 6/2010 do Banco de Portugal que recomenda o cálculo dos rácios core tier 1, tier 1 e tier 2 usando o método notações internas (IRB) de avaliação da exposição do banco aos riscos de crédito, operacional, etc. e onde os ativos ponderados pelo risco são calculados como 12,5 vezes o valor dos requisitos totais de fundos calculados pelo banco. Este método é baseado nas recomendações do Basel II que serão substituídas pelo Basel III. Dado que um dos principais motivos para a crise económica e financeira que assolou o mundo em 2007 foi a acumulação de alavancagem excessiva e gradual erosão da qualidade da base do capital próprio dos bancos, é importante analisar a posição dos bancos Portugueses, que embora não sejam muito grandes a nível global, controlam a economia do país. Espera-se que com a implementação das recomendações do Basel III não haja no futuro uma repetição dos choques sistémicos de 2007. Os resultados deste estudo usando o método padrão recomendado pelo BCBS mostram que de catorze bancos Portugueses incluídos neste estudo, apenas seis (BES, Montepio, Finantia, BIG, Invest e BIC) conseguem enquadrar nas recomendações mínimas do Basel III até 1-1- 2019 e alguns outros estão marginalmente abaixo dos rácios mínimos (CGD, Itaú e Crédito Agrícola).

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Coastal low-level jets (CLLJ) are a low-tropospheric wind feature driven by the pressure gradient produced by a sharp contrast between high temperatures over land and lower temperatures over the sea. This contrast between the cold ocean and the warm land in the summer is intensified by the impact of the coastal parallel winds on the ocean generating upwelling currents, sharpening the temperature gradient close to the coast and giving rise to strong baroclinic structures at the coast. During summertime, the Iberian Peninsula is often under the effect of the Azores High and of a thermal low pressure system inland, leading to a seasonal wind, in the west coast, called the Nortada (northerly wind). This study presents a regional climatology of the CLLJ off the west coast of the Iberian Peninsula, based on a 9km resolution downscaling dataset, produced using the Weather Research and Forecasting (WRF) mesoscale model, forced by 19 years of ERA-Interim reanalysis (1989-2007). The simulation results show that the jet hourly frequency of occurrence in the summer is above 30% and decreases to about 10% during spring and autumn. The monthly frequencies of occurrence can reach higher values, around 40% in summer months, and reveal large inter-annual variability in all three seasons. In the summer, at a daily base, the CLLJ is present in almost 70% of the days. The CLLJ wind direction is mostly from north-northeasterly and occurs more persistently in three areas where the interaction of the jet flow with local capes and headlands is more pronounced. The coastal jets in this area occur at heights between 300 and 400 m, and its speed has a mean around 15 m/s, reaching maximum speeds of 25 m/s.

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The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, there were identified five broad selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. After the identification criteria, a survey was elaborated and companies were contacted in order to understand which factors have more weight in their decisions to choose the partners. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP) method or Value Analysis. The goal of the paper it's to supply a selection reference model that can represent an orientation/pattern for a decision making on the suppliers/partners selection process

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RESUMO - A 8 de Maio de 2008 surgiu o centro de atendimento “Linha Saúde24” (S24) no sentido de modernizar o SNS, aproximando-o do cidadão. O serviço surge baseado no modelo inglês – o NHS Direct – que pode ser encarado como um serviço de informação telefónico apoiado por enfermeiros, disponível 24h por dia, concebido para expandir os serviços púbicos de acesso à rede prestadora de cuidados com intuito de aliviar a pressão da procura na rede de urgências hospitalares e médicos de família, assim como diluir as iniquidades regionais na prestação de serviços. A S24 assenta na perspectiva de ser um ponto de contacto inicial do utente com a rede de prestação de cuidados de saúde com capacidade de orientação. O objectivo da linha está na tentativa mais eficiente no uso dos recursos disponíveis, ao mesmo tempo que delega responsabilidade no cidadão na forma como este utiliza os recursos disponíveis, com melhor racionalização financeira na área da saúde aliada a uma melhor qualidade de serviço prestada e adequada, colocando os cidadãos no mesmo patamar, diluindo as dificuldades de acesso a aqueles que necessitam na tentativa de harmonizar e racionalizar o consumo de serviços de saúde. Esta estrutura permite ao cidadão conhecer melhor o seu estado de saúde e decidir mais acertadamente quanto à decisão a tomar. Com este estudo, e com base na literatura nacional e internacional, pretende-se descrever o perfil de utilizador que acede à S24 – definir o tipo de utilizador, disposição geográfica, motivos pelo qual acede ao serviço e qual o seu destino final, fazendo comparação com o perfil do NHS Direct. Assim, e com os dados obtidos, far-se-á uma avaliação preliminar em termos do contributo da linha S24 no que concerne à sua eficiência, equidade e empowerment dado ao utilizador. --- ------------------------------ABSTRACT - Saúde 24 (S24) is a national 24-hour health line initiated in May 2008 aiming at modernizing the Portuguese NHS by bringing it closer to the citizen. Indeed, S24 be seen as an initial contact point between the patient and the healthcare network, facilitating a better a management of health care demand. The service is inspired on the UK NHS Direct – a nurse-led telephone line to provide easier and faster advice information to people about health, illness and NHS services. It is expected to provide information so that people can deal with their health problems or their families´ on their own, with the purpose of reducing demand to A&E department and out-of-hours GP services. Additionally it can contribute to a reduction in regional inequities in healthcare provision through bringing health care advice to remote areas. The purpose of S24 is to handle more efficiently the available resources by enabling responsibilities in citizens. By doing so, S24 encourages a more appropriate use of available resources, with better financial outcomes and a better quality of care. It is meant, in terms of empowerment, to help people to be in control of their health and healthcare interactions by participating in the final decision. Based on quantitative data, this study defines the S24 caller user profile in terms of type, geographical reference, reasons for calling and outcome. This analysis allows us to perform a preliminary evaluation of the S24 in terms of its contribution to efficiency, equity and empowerment. Then the S24 is compared to

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Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

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Dissertação de Mestrado apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação do Doutor Carlos Quelhas Martins

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Dissertation to obtain the degree of Master in Electrical and Computer Engineering

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics