Porto: cidade amiga das pessoas idosas. Um estudo centrado na perspectiva de idosos das freguesias de S. Nicolau e Sé

Uma cidade amiga das pessoas idosas é um meio urbano onde são proporcionadas condições de saúde, segurança e participação que permitem às pessoas mais velhas envelhecerem activamente e viverem com dignidade. A nossa investigação, de natureza qualitativa e exploratória, teve como objectivo verificar se a cidade do Porto possui características de uma cidade amiga das pessoas idosas, na perspectiva de idosos residentes neste meio urbano. Para tal, realizamos dois focus groups com idosos habitantes nas Freguesias de S. Nicolau e Sé, seleccionados a partir de uma amostragem por conveniência, tendo sido utilizado um guião de entrevista constituído pelas categorias: espaços exteriores e edifícios; transportes; habitação; respeito e inclusão social; participação social; participação cívica e emprego; comunicação e informação; apoio comunitário e serviços de saúde. No nosso estudo, foi possível constatar que os participantes, apesar de se manifestarem genericamente satisfeitos com a sua vida na cidade do Porto e identificarem algumas características desse meio urbano que podem ser consideradas como amigas das pessoas idosas, descreveram um vasto conjunto de condições da cidade que limitam o seu quotidiano. Neste sentido, relativamente aos espaços exteriores, para além de os caracterizarem como inseguros quanto ao crime, reconheceram essencialmente limitações à sua mobilidade e segurança física, tais como os declives acentuados e as irregularidades do terreno de certos passeios, o curto período de tempo proporcionado para que sejam atravessadas algumas passadeiras e o aglomerar de lixo e estacionamento de veículos em locais destinados a peões. Adicionalmente, os participantes manifestaram-se insatisfeitos com o número de autocarros e paragens disponíveis na sua freguesia e identificaram nas habitações existentes na cidade do Porto um elevado nível de degradação estrutural e uma falta generalizada de condições de conforto, acessibilidade e protecção face a condições atmosféricas. Em oposição, foi possível verificar que a maior parte dos participantes se sente respeitado e incluído nas actividades e eventos realizados na sua comunidade. Da mesma forma, mostraram-se satisfeitos com a variedade de actividades em que têm oportunidade de participar, incluindo actividades de voluntariado e trabalho não remunerado. Aspectos característicos de uma cidade amiga do idoso, tais como a aglomeração geográfica dos edifícios públicos e lojas e a existência de serviços de apoio comunitário foram também identificados.

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.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

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 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.

Zonal prices analysis supported by a data mining based methodology

A methodology based on data mining techniques to support the analysis of zonal prices in real transmission networks is proposed in this paper. The mentioned methodology uses clustering algorithms to group the buses in typical classes that include a set of buses with similar LMP values. Two different clustering algorithms have been used to determine the LMP clusters: the two-step and K-means algorithms. In order to evaluate the quality of the partition as well as the best performance algorithm adequacy measurements indices are used. The paper includes a case study using a Locational Marginal Prices (LMP) data base from the California ISO (CAISO) in order to identify zonal prices.

Multi-dimensional scaling applied to histogram-based DNA analysis

This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendall τ rank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

A multidimensional scaling analysis of musical sounds based on pseudo phase plane

This paper studies musical opus from the point of view of three mathematical tools: entropy, pseudo phase plane (PPP), and multidimensional scaling (MDS). The experiments analyze ten sets of different musical styles. First, for each musical composition, the PPP is produced using the time series lags captured by the average mutual information. Second, to unravel hidden relationships between the musical styles the MDS technique is used. The MDS is calculated based on two alternative metrics obtained from the PPP, namely, the average mutual information and the fractal dimension. The results reveal significant differences in the musical styles, demonstrating the feasibility of the proposed strategy and motivating further developments towards a dynamical analysis of musical sounds.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.

Epidemiology of physiotherapy utilization among adults and elderly

OBJECTIVE: To analyze the prevalence of physiotherapy utilization and to explore the variables associated to its utilization. METHODS: A population-based cross-sectional study, including 3,100 subjects aged 20 years or more living in the urban area of Pelotas, southern Brazil, was carried out. The sample was selected following a multiple-stage protocol; the census tracts delimited by the Instituto Brasileiro de Geografia e Estatística (Brazilian Institute of Geography and Statistics) were the primary sample units. Following descriptive and crude analyses, Poisson regression models taking the clustering of the sample into account were carried out. Data were collected through face-to-face interviews using a standardized and pre-tested questionnaire. RESULTS: The lifetime utilization of physiotherapy was 30.2%; and physiotherapy utilization in the 12 months prior to the interview was reported by 4.9%. Women, elderly subjects, and those from higher socioeconomic levels were more likely to use physiotherapy. Restricting analysis to subjects who attended physiotherapy, 66% used public health services, 25% used insurance health services and 9% had private sessions. CONCLUSIONS: This is the first population-based study on physiotherapy utilization carried out in Brazil. Utilization of physio therapy was lower than reported in both developed and developing countries. The study findings might help public health authorities to organize healthcare service in terms of this important demand.

Análise de clusters e volatilidade de índices de acções

Mestrado em Contabilidade e Gestão das Instituições Financeiras

Cluster Analysis of Business Data

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Agrupamento de dados com restrições

Mestrado em Engenharia Informática