Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

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.

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.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

Providing energy efficiency location-based strategies for buildings using linked open data

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

Classificação de sons urbanos usando motifs e MFCC

A classificação automática de sons urbanos é importante para o monitoramento ambiental. Este trabalho apresenta uma nova metodologia para classificar sons urbanos, que se baseia na descoberta de padrões frequentes (motifs) nos sinais sonoros e utiliza-los como atributos para a classificação. Para extrair os motifs é utilizado um método de descoberta multi-resolução baseada em SAX. Para a classificação são usadas árvores de decisão e SVMs. Esta nova metodologia é comparada com outra bastante utilizada baseada em MFCC. Para a realização de experiências foi utilizado o dataset UrbanSound disponível publicamente. Realizadas as experiências, foi possível concluir que os atributos motif são melhores que os MFCC a discriminar sons com timbres semelhantes e que os melhores resultados são conseguidos com ambos os tipos de atributos combinados. Neste trabalho foi também desenvolvida uma aplicação móvel para Android que permite utilizar os métodos de classificação desenvolvidos num contexto de vida real e expandir o dataset.

Descoberta de Conhecimento em Dádivas de Sangue

Atualmente, são geradas enormes quantidades de dados que, na maior parte das vezes, não são devidamente analisados. Como tal, existe um fosso cada vez mais significativo entre os dados existentes e a quantidade de dados que é realmente analisada. Esta situação verifica-se com grande frequência na área da saúde. De forma a combater este problema foram criadas técnicas que permitem efetuar uma análise de grandes massas de dados, retirando padrões e conhecimento intrínseco dos dados. A área da saúde é um exemplo de uma área que cria enormes quantidades de dados diariamente, mas que na maior parte das vezes não é retirado conhecimento proveitoso dos mesmos. Este novo conhecimento poderia ajudar os profissionais de saúde a obter resposta para vários problemas. Esta dissertação pretende apresentar todo o processo de descoberta de conhecimento: análise dos dados, preparação dos dados, escolha dos atributos e dos algoritmos, aplicação de técnicas de mineração de dados (classificação, segmentação e regras de associação), escolha dos algoritmos (C5.0, CHAID, Kohonen, TwoSteps, K-means, Apriori) e avaliação dos modelos criados. O projeto baseia-se na metodologia CRISP-DM e foi desenvolvido com a ferramenta Clementine 12.0. O principal intuito deste projeto é retirar padrões e perfis de dadores que possam vir a contrair determinadas doenças (anemia, doenças renais, hepatite, entre outras) ou quais as doenças ou valores anormais de componentes sanguíneos que podem ser comuns entre os dadores.

Text Mining: análise de sentimentos na classificação de notícias

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Adapter module for self-learning production systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e Computadores

Modelos de negócios para a internet das coisas baseados em big data : Muzzley, um estudo de caso

A Internet das Coisas tal como o Big Data e a análise dos dados são dos temas mais discutidos ao querermos observar ou prever as tendências do mercado para as próximas décadas, como o volume económico, financeiro e social, pelo que será relevante perceber a importância destes temas na atualidade. Nesta dissertação será descrita a origem da Internet das Coisas, a sua definição (por vezes confundida com o termo Machine to Machine, redes interligadas de máquinas controladas e monitorizadas remotamente e que possibilitam a troca de dados (Bahga e Madisetti 2014)), o seu ecossistema que envolve a tecnologia, software, dispositivos, aplicações, a infra-estrutura envolvente, e ainda os aspetos relacionados com a segurança, privacidade e modelos de negócios da Internet das Coisas. Pretende-se igualmente explicar cada um dos “Vs” associados ao Big Data: Velocidade, Volume, Variedade e Veracidade, a importância da Business Inteligence e do Data Mining, destacando-se algumas técnicas utilizadas de modo a transformar o volume dos dados em conhecimento para as empresas. Um dos objetivos deste trabalho é a análise das áreas de IoT, modelos de negócio e as implicações do Big Data e da análise de dados como elementos chave para a dinamização do negócio de uma empresa nesta área. O mercado da Internet of Things tem vindo a ganhar dimensão, fruto da Internet e da tecnologia. Devido à importância destes dois recursos e á falta de estudos em Portugal neste campo, com esta dissertação, sustentada na metodologia do “Estudo do Caso”, pretende-se dar a conhecer a experiência portuguesa no mercado da Internet das Coisas. Visa-se assim perceber quais os mecanismos utilizados para trabalhar os dados, a metodologia, sua importância, que consequências trazem para o modelo de negócio e quais as decisões tomadas com base nesses mesmos dados. Este estudo tem ainda como objetivo incentivar empresas portuguesas que estejam neste mercado ou que nele pretendam aceder, a adoptarem estratégias, mecanismos e ferramentas concretas no que diz respeito ao Big Data e análise dos dados.

Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.

Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

Electric vehicle assistant based in driver profile

This paper presents the outcomes of a research work consisting in the development of an Electric Vehicle Assistant (EVA), which creates and stores a driver profile where are contained the driving behaviours related with the EV energy consumption, the EV battery charging information, and the performed routes. This is an application for mobile devices that is able to passively track the driver behaviour and to access several information related with the EV in real time. It is also proposed a range prediction approach based on probability to take into account unpredictable effects of personal driving style, traffic or weather.