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.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

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.

Online versus postal questionnaires: a comparison between two data collection methods

Objectives : The purpose of this article is to find out differences between surveys using paper and online questionnaires. The author has deep knowledge in the case of questions concerning opinions in the development of survey based research, e.g. the limits of postal and online questionnaires. Methods : In the physician studies carried out in 1995 (doctors graduated in 1982-1991), 2000 (doctors graduated in 1982-1996), 2005 (doctors graduated in 1982-2001), 2011 (doctors graduated in 1977-2006) and 457 family doctors in 2000, were used paper and online questionnaires. The response rates were 64%, 68%, 64%, 49% and 73%, respectively. Results : The results of the physician studies showed that there were differences between methods. These differences were connected with using paper-based questionnaire and online questionnaire and response rate. The online-based survey gave a lower response rate than the postal survey. The major advantages of online survey were short response time; very low financial resource needs and data were directly loaded in the data analysis software, thus saved time and resources associated with the data entry process. Conclusions : The current article helps researchers with planning the study design and choosing of the right data collection method.

European information users: topics from a comparative analysis within the European Union

Purpose – The aim of this article is to present some results from research undertaken into the information behaviour of European Documentation Centre (EDC) users. It will reflect on the practices of a group of 234 users of 55 EDCs covering 21 Member States of the European Union (EU), used to access European information. Design/methodology/approach – In order to collect the data presented here, five questionnaires were sent to users in all the EDCs in Finland, Ireland, Hungary and Portugal. In the remaining EU countries, five questionnaires were sent to two EDCs chosen at random. The questionnaires were sent by post, following telephone contact with the EDC managers. Findings – Factors determining access to information on the European Union and the frequency of this access are identified. The information providers most commonly used to access European information and the information sources considered the most reliable by respondents will also be analysed. Another area of analysis concerns the factors cited by respondents as facilitating access to information on Europe or, conversely, making it more difficult to access. Parallel to this, the aspects of accessing information on EU that are valued most by users will also be assessed. Research limitations/implications – Questionnaires had to be used, as the intention was to cover a very extensive geographical area. However, in opting for closed questions, it is acknowledged that standard responses have been obtained with no scope for capturing the individual circumstances of each respondent, thus making a qualitative approach difficult. Practical implications – The results provide an overall picture of certain aspects of the information behaviour of EDC users. They may serve as a starting point for planning training sessions designed to develop the skills required to search, access, evaluate and apply European information within an academic context. From a broader perspective, they also constitute factors which the European Commission should take into consideration when formulating its information and communication policy. Originality/value – This is the first piece of academic research into the EDCs and their users, which aimed to cover all Members State of the EU.

SmartClean: an incremental data cleaning tool

This paper presents the SmartClean tool. The purpose of this tool is to detect and correct the data quality problems (DQPs). Compared with existing tools, SmartClean has the following main advantage: the user does not need to specify the execution sequence of the data cleaning operations. For that, an execution sequence was developed. The problems are manipulated (i.e., detected and corrected) following that sequence. The sequence also supports the incremental execution of the operations. In this paper, the underlying architecture of the tool is presented and its components are described in detail. The tool's validity and, consequently, of the architecture is demonstrated through the presentation of a case study. Although SmartClean has cleaning capabilities in all other levels, in this paper are only described those related with the attribute value level.

Ontologies for reusing data cleaning knowledge

The emergence of new business models, namely, the establishment of partnerships between organizations, the chance that companies have of adding existing data on the web, especially in the semantic web, to their information, led to the emphasis on some problems existing in databases, particularly related to data quality. Poor data can result in loss of competitiveness of the organizations holding these data, and may even lead to their disappearance, since many of their decision-making processes are based on these data. For this reason, data cleaning is essential. Current approaches to solve these problems are closely linked to database schemas and specific domains. In order that data cleaning can be used in different repositories, it is necessary for computer systems to understand these data, i.e., an associated semantic is needed. The solution presented in this paper includes the use of ontologies: (i) for the specification of data cleaning operations and, (ii) as a way of solving the semantic heterogeneity problems of data stored in different sources. With data cleaning operations defined at a conceptual level and existing mappings between domain ontologies and an ontology that results from a database, they may be instantiated and proposed to the expert/specialist to be executed over that database, thus enabling their interoperability.

Quais os atributos que um auditor interno deve ter

Dissertação apresentada ao Instituto Superior de Contabilidade para a obtenção do Grau de Mestre em Auditoria

Crianças com défices no controlo motor: contributo para a elaboração do core set da Classificação Internacional de Funcionalidade, Incapacidade e Saúde (CIF)

A CIF é um sistema de classificação adotado pela OMS, que serve de referência universal para descrever, avaliar e medir saúde e incapacidade, a nível individual e ao nível da população. Contudo, apesar do interesse internacional gerado em torno da CIF, esta é considerada uma classificação complexa e extensa, fato que despoletou a criação de core sets – listas de itens da CIF especificamente selecionados pela sua relevância na descrição e qualificação de uma determinada condição de saúde – como resposta a esta problemática. Até à data, foram desenvolvidos core sets para várias patologias comuns. Contudo, apesar do controlo motor ser uma área de investigação muito reconhecida nos últimos 20 anos, ainda não possui um core set próprio. Assim, o objetivo deste estudo é contribuir para o desenvolvimento de um core set, com base na CIF-CJ, dirigido para uma descrição abrangente das competências inerentes a crianças, dos 6 aos 18 anos de idade, com défices no controlo motor. Deste modo, recorreu-se a uma revisão da literatura sobre a temática em estudo, de modo a reunir informação para a construção de uma proposta a core set, posteriormente sujeita ao escrutínio de peritos, através do recurso ao método de Delphi. Após várias rondas, foi alcançado um consenso acerca da lista final de códigos CIF que constituem o core set final.

A relação terapeuta-cliente: na perspectiva do cliente

O presente estudo resulta de uma crescente preocupação e percepção da importância da relação terapeuta-cliente e procura compreender a perspectiva do cliente e a forma como este sente e vive essa relação ao longo de todo o seu processo terapêutico. O estudo realizado, de natureza qualitativa e de carácter exploratório, visa sobretudo analisar a influência da relação terapeuta-cliente na perspectiva do cliente, identificar e descrever os fatores que a influenciam e perceber a mais-valia desta relação ao longo do processo terapêutico. Para tal, foram entrevistadas dezanove pessoas que estão a receber tratamento de terapia ocupacional. Para a recolha dos dados foi aplicada a entrevista semi-estruturada por se pretender um contributo mais envolvente, particularizado e subjetivo dos clientes. Através da realização do estudo e de acordo com a perspetiva dos clientes entrevistados, concluiu-se que a capacidade do terapeuta em construir e estabelecer um vínculo com o cliente ditará grande parte do sucesso ou insucesso do processo terapêutico. Porém, tão importante como formar e estabelecer um vínculo terapeuta-cliente é preciso ter a preocupação contínua de o manter sempre vivo e fortalecido. Para que tal aconteça, o terapeuta não pode menosprezar a experiência de vida e expectativas do cliente e deve assumir um papel de permanente preocupação e atenção a todas as transformações quotidianas pois estas influenciam o envolvimento do cliente nas suas atividades/ocupações e afetam a sua saúde e desempenho. O novo milénio requer dos profissionais de saúde em geral e dos terapeutas ocupacionais em particular, novas habilidades e competências. É fundamental adquirir a consciência de que os clientes são o centro de todo o processo terapêutico. É necessário ter uma visão holística e não fragmentada do cliente. É importante interagir com os clientes e permitir uma troca de conhecimento, entre o saber do cliente e o saber do terapeuta. Essa troca gera convergências, fortalece laços e o processo terapêutico avança. Nesse relacionamento, ambos aprendem, progridem e crescem.

Previsão de vento baseado em técnicas de Data Mining

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia