Prospecção de recursos hidrominerais e geotérmicos na Orla Ocidental Portuguesa: os casos das Termas dos Cucos e de Monte Real

A cobertura sedimentar da região Oeste portuguesa é constituída por uma série possante de sedimentos com uma variedade de fácies com idades compreendidas entre o Triásico Superior e o actual. Estes sedimentos foram depositados numa bacia alongada com direcção NNE‐SSW. A tectónica desta cobertura sedimentar é condicionada pelas falhas tardi‐Variscas que afectaram o substrato e pelo complexo evaporítico depositado na base das séries sedimentares. Séries evaporíticas espessas de idade Hetangiana formaram numerosas estruturas diapíricas. Na região Oeste de Portugal existem diversas nascentes minerais e termais usadas para hidroterapia. Esta tese tem por objectivo estudar as concessões hidrominerais das Termas dos Cucos e das Termas de Monte Real (Portugal Central), bem como as suas áreas envolventes. Estas actividades hidroterapêuticas são muito relevantes em termos sócio‐económicos para os concelhos de Torres Vedras e Leiria. Os estudos contemplados nesta tese (que incluíram trabalho de campo no domínio da geotectónica, geomorfologia e hidrogeologia) permitiram delinear modelos hidrogeológicos conceptuais, apoiados, ainda, pela re‐interpretação de estudos geofísicos e hidrogeológicos prévios. A caracterização destas áreas foi suportada por inventários hidrogeológicos, tendo sido determinante para o projecto dos furos de captação, incluindo localização e profundidade. Todos os dados compilados foram representados cartograficamente numa base de Sistemas de Informação Geográfica (SIG).

Geotecnia urbana da zona ribeirinha de Vila Nova de Gaia: criação de uma base de dados SIG geotécnica

O presente trabalho apresenta os resultados dos estudos geotécnicos e de uma base de dados da zona ribeirinha de Vila Nova de Gaia, com o objectivo de compreender melhor os aspectos geotécnicos em ambiente urbano numa área sensível com um registo histórico de instabilidade de taludes rochosos. Além disso, os escassos estudos científicos recentes de natureza geológica e geotécnica em Vila Nova de Gaia justificam o estudo exploratório da geotecnia urbana da zona ribeirinha de Vila Nova de Gaia. A importância de Vila Nova de Gaia como a terceira maior cidade portuguesa e como centro de intensa actividade económica e cultural despoleta uma constante necessidade de expansão. O aumento da densidade populacional acarreta a realização de projectos complexos de engenharia, utilizando o subsolo para a construção e, com frequência, em terrenos com características geotécnicas desfavoráveis. As cidades de Vila Nova de Gaia e do Porto foram sendo edificadas ao longo de encostas numa plataforma litoral caracterizada por uma vasta área aplanada, inclinando ligeiramente para Oeste. Esta plataforma foi cortada pelo Rio Douro num vale encaixado de vertentes abruptas, nas quais se localizam as zonas ribeirinhas das duas cidades. Este trabalho envolveu, inicialmente, uma caracterização topográfica, morfoestrutural, geotectónica e geomecânica da área de estudo e, numa fase posterior, o desenvolvimento duma base de dados geotécnica. Todos os dados geológicos e geotécnicos locais e os estudos geotécnicos levados a cabo in situ pelas diversas empresas e instituições foram representados cartograficamente numa base apoiada pelos Sistemas de Informação Geográfica (SIG). Esta metodologia inter‐disciplinar foi de grande valor para um melhor conhecimento dos riscos geológico‐geotécnicos ao longo das margens do Rio Douro. De facto, a cartografia geotécnica da zona ribeirinha de Vila Nova de Gaia deve constituir uma ferramenta importante para uma previsão mais rigorosa de futuras instabilidades de taludes e um bom instrumento para a gestão do espaço urbano.

Geotecnia urbana da zona ribeirinha de Vila Nova de Gaia (Cais de Gaia, Santa Marinha – Lavadores, Canidelo): uma avaliação preliminar

Mestrado em Engenharia Geotécnica e Geoambiente

Cartografia de zonamento geotécnico no apoio ao planeamento e valorização da exploração do georrecurso: o caso do maciço rochoso da Curviã (Joane)

Este trabalho centra-se no estudo do aproveitamento expectável do maciço rochoso da pedreira da Curviã N.o 2 (Joane, Vila Nova de Famalicão, no N Portugal), através da obtenção de um bloco unitário tipo que forneça indicações para a exploração do recurso geológico para fins industriais e/ou ornamentais. Desta forma, investiga-se se num dado limite de zona geotécnica do maciço rochoso e propicio a obtenção de blocos com dimensão, avaliados apos o processo de transformação, nomeadamente, para enrocamento em obras marítimas ou balastro em obras ferroviárias. Foram seleccionados diversos afloramentos, tendo-se recorrido a técnica de amostragem linear as superfícies expostas do maciço. Esta técnica e uma das formas mais expeditas de coligir dados geológico-geotécnicos relativos as descontinuidades. Procedeu-se, ainda, a um tratamento estatístico das descontinuidades, bem como dos parâmetros geológico-geotécnicos e geomecânicos a estas associadas, propostos pela Sociedade Internacional de Mecânica das Rochas (ISRM). Todos os dados foram representados cartograficamente numa base apoiada pelos Sistemas de Informação Geográfica (SIG) e utilizadas as ferramentas de geologia estrutural, analise morfotectónica, modelação digital de terreno e cartografia de zonamento geotécnico. O zonamento geotécnico do maciço granítico foi realizado sempre em estreita ligação com o conhecimento das características do maciço ”in situ”. Pretende-se que esta metodologia contribua para um melhor conhecimento da compartimentação dos maciços rochosos em geral e, em particular, do modelo geotécnico comportamental do maciço rochoso da Curviã N.o2.

SNC / NCRF 24 – Acontecimentos após a data do balanço e suas implicações fiscais e de auditoria

Orientador Prof. Dr. João Domingues Costa

Geostatistical Mapping of Outfall Plume Dispersion Data gathered with an autonomous underwater vehicle

The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron’s classical estimator was used the compute the experimental semivariogram which was fitted to three theoretical models: spherical, exponential and gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the near by beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume’s dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

Norma internacional de contabilidade [NIC 10 ]: acontecimento após a data de balanço

Revista Fiscal Maio 2006

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Adaptive tool for automatic data collection of real electricity markets

The study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

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.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.