Automatic Electricity Markets Data Extraction for Realistic Multi-agent Simulations

Electricity markets worldwide suffered profound transformations. The privatization of previously nationally owned systems; the deregulation of privately owned systems that were regulated; and the strong interconnection of national systems, are some examples of such transformations [1, 2]. In general, competitive environments, as is the case of electricity markets, require good decision-support tools to assist players in their decisions. Relevant research is being undertaken in this field, namely concerning player modeling and simulation, strategic bidding and decision-support.

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.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Towards a unified European electricity market: The contribution of data-mining to support realistic simulation studies

Worldwide electricity markets have been evolving into regional and even continental scales. The aim at an efficient use of renewable based generation in places where it exceeds the local needs is one of the main reasons. A reference case of this evolution is the European Electricity Market, where countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. The continuous transformations electricity markets have been experiencing over the years create the need to use simulation platforms to support operators, regulators, and involved players for understanding and dealing with this complex environment. This paper focuses on demonstrating the advantage that real electricity markets data has for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations will bring to the participant countries. A case study using MASCEM (Multi-Agent System for Competitive Electricity Markets) is presented, with a scenario based on real data, simulating the European Electricity Market environment, and comparing its performance when using several different market mechanisms.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

IgM and IgA Antibody Responses in 12 Cases of Human Acquired Toxoplasmosis

The persistence, in some subjects, of specific IgM antibodies to Toxoplasma gondii for several months after the acute phase of infection has complicated the interpretation of serological test results for toxoplasmosis. Several reports have emphasized the value of the detection of Toxoplasma-specific IgA antibodies for the diagnosis of acute toxoplasmosis. In this article, we report the follow-up profiles of Toxoplasma-specific IgM and IgA antibodies in serum samples obtained from 12 patients at various intervals after the onset of the clinical manifestations of infection. IgM antibodies were detected by the indirect immunofluorescence (IIF) test, antibody capture enzyme-linked immunosorbent assay (cELISA) and enzyme-mediated chemilluminescent technique (CmL). IgA antibodies were quantified by the direct ELISA (dELISA) and cELISA procedures. As defined by the manufacturer of the cELISA test for IgA used, most patients with acute toxoplasmosis have antibody levels > 40 arbritary units per ml (AU/ml). At values > 40 AU/ml, the cELISA for IgA detected significant antibody levels for a shorter time than the other techniques used for IgM and IgA detection. However, IgA levels £ 40 AU/ml do not exclude the possibility of acute toxoplasmosis since such levels can be reached very soon after infection with T. gondii. The results obtained in the present study show that the serological diagnosis of acute toxoplasmosis may not be such an easy task. Our data suggest that use of the IgA-cELISA concomitantly with IgM antibody screening could permit, in some circumstances, a more efficient diagnosis of acute acquired toxoplasmosis

Improving discovery in the life sciences using semantic Web technologies and linked data: design principles for life sciences knowledge organization systems

Dissertation presented to obtain the Ph.D degree in Bioinformatics

Declarative approach to data extraction of web pages

Thesis submitted to Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, in partial fulfilment of the requirements for the degree of Master in Computer Science

Implementação de uma Interface Homem-máquina baseada em Eletroculografia

A constante evolução da tecnologia permitiu ao ser humano a utilização de dispositivos electrónicos nas suas rotinas diárias. Estas podem ser afetadas quando os utilizadores sofrem de deficiências ou doenças que afetam as suas capacidades motoras. Com o intuito de minimizar este obstáculo surgiram as Interfaces Homem-Computador (HCI). É neste panorama que os sistemas HCI baseados em Eletroculografia (EOG) assumem um papel preponderante na melhoria da qualidade de vida destes indivíduos. A Eletroculografia é o resultado da aquisição do movimento ocular, que pode ser adquirido através de diversos métodos. Os métodos mais convencionais utilizam elétrodos de superfície para aquisição dos sinais elétricos, ou então, utilizam sistemas de gravação de vídeo, que gravam o movimento ocular. O objetivo desta tese é desenvolver um sistema HCI baseado em Eletroculografia, que adquire o sinal elétrico do movimento ocular através de elétrodos de superfície. Para tal desenvolveu-se um circuito eletrónico para a aquisição do sinal de EOG, bem como um algoritmo em Python para análise do mesmo. O circuito foi desenvolvido recorrendo a seis módulos diferentes, cada um deles com uma função específica. Para cada módulo foi necessário desenhar e implementar placas de circuito impresso, que quando conectadas entre si permitem filtrar, amplificar e digitalizar os sinais elétricos, adquiridos através de elétrodos de superfície, originados pelo movimento ocular. O algoritmo criado em Python permite analisar os dados provenientes do circuito e converte-os para coordenadas. Através destas foi possível determinar o sentido e a amplitude do movimento ocular.

Exploring distributed computing tools through data mining tasks

Harnessing idle PCs CPU cycles, storage space and other resources of networked computers to collaborative are mainly fixated on for all major grid computing research projects. Most of the university computers labs are occupied with the high puissant desktop PC nowadays. It is plausible to notice that most of the time machines are lying idle or wasting their computing power without utilizing in felicitous ways. However, for intricate quandaries and for analyzing astronomically immense amounts of data, sizably voluminous computational resources are required. For such quandaries, one may run the analysis algorithms in very puissant and expensive computers, which reduces the number of users that can afford such data analysis tasks. Instead of utilizing single expensive machines, distributed computing systems, offers the possibility of utilizing a set of much less expensive machines to do the same task. BOINC and Condor projects have been prosperously utilized for solving authentic scientific research works around the world at a low cost. In this work the main goal is to explore both distributed computing to implement, Condor and BOINC, and utilize their potency to harness the ideal PCs resources for the academic researchers to utilize in their research work. In this thesis, Data mining tasks have been performed in implementation of several machine learning algorithms on the distributed computing environment.

Investigating habitat association of breeding birds using public domain satellite imagery and land cover data.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Deteção e extinção de incêndio em Data Center

O presente trabalho enquadra-se na temática de segurança contra incêndio em edifícios e consiste num estudo de caso de projeto de deteção e extinção de incêndio num Data Center. Os objetivos deste trabalho resumem-se à realização de um estudo sobre o estado da arte da extinção e deteção automática de incêndio, ao desenvolvimento de uma ferramenta de software de apoio a projetos de extinção por agentes gasosos, como também à realização de um estudo e uma análise da proteção contra incêndios em Data Centers. Por último foi efetuado um estudo de caso. São abordados os conceitos de fogo e de incêndio, em que um estudo teórico à temática foi desenvolvido, descrevendo de que forma pode o fogo ser originado e respetivas consequências. Os regulamentos nacionais relativos à Segurança Contra Incêndios em Edifícios (SCIE) são igualmente abordados, com especial foco nos Sistemas Automáticos de Deteção de Incêndio (SADI) e nos Sistemas Automáticos de Extinção de Incêndio (SAEI), as normas nacionais e internacionais relativas a esta temática também são mencionadas. Pelo facto de serem muito relevantes para o desenvolvimento deste trabalho, os sistemas de deteção de incêndio são exaustivamente abordados, mencionando características de equipamentos de deteção, técnicas mais utilizadas como também quais os aspetos a ter em consideração no dimensionamento de um SADI. Quanto aos meios de extinção de incêndio foram mencionados quais os mais utilizados atualmente, as suas vantagens e a que tipo de fogo se aplicam, com especial destaque para os SAEI com utilização de gases inertes, em que foi descrito como deve ser dimensionado um sistema deste tipo. Foi também efetuada a caracterização dos Data Centers para que seja possível entender quais as suas funcionalidades, a importância da sua existência e os aspetos gerais de uma proteção contra incêndio nestas instalações. Por último, um estudo de caso foi desenvolvido, um SADI foi projetado juntamente com um SAEI que utiliza azoto como gás de extinção. As escolhas e os sistemas escolhidos foram devidamente justificados, tendo em conta os regulamentos e normas em vigor.