Browsing by Author "Duarte, João"
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- Agrupamento de dados com restriçõesPublication . Duarte, João; Duarte, Fernando Jorge Ferreira; Fred, AnaAs técnicas de agrupamento de dados (classificação não supervisionada) são úteis em vários problemas de análise exploratória de dados, tomada de decisão, estruturação de documentos e segmentação de imagem, entre outros. O seu objectivo consiste na divisão de um conjunto de dados em vários grupos, em que dados semelhantes são colocados no mesmo grupo e dados dissemelhantes em grupos diferentes. A combinação de agrupamentos de dados surgiu na última década com o intuito de melhorar a robustez e qualidade do agrupamento de dados, reutilizar soluções e agrupar dados de forma distribuída. O agrupamento de dados com restrições tem como objectivo incorporar conhecimento a priori no processo de agrupamento de dados, com o intuito de aumentar a qualidade do agrupamento de dados e, simultaneamente, encontrar soluções apropriadas a tarefas ou interesses específicos. Nesta dissertação, são estudados vários tipos de restrições usadas no agrupamento de dados, assim como os principais algoritmos de agrupamento de dados com restrições. São também desenvolvidas formas de combinar vários agrupamentos de dados usando restrições num agrupamento de dados final. Com o propósito de comparar os algoritmos de agrupamento com restrições e de avaliar os métodos de combinação de agrupamentos de dados com restrições propostos, são realizados dois estudos comparativos usando conjuntos de dados de referência.
- A Data Mining Framework for Electric Load ProfilingPublication . Ramos, Sérgio; Duarte, João; Duarte, F. Jorge; Vale, Zita; Faria, PedroThis paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.
- A Data-mining-based Methodology to support MV Electricity Customers' CharacterizationPublication . Ramos, Sérgio; Duarte, João; Duarte, F. Jorge; Vale, ZitaThis 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.
- Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using SubsamplingPublication . Duarte, Jorge; Fred, Ana; Rodrigues, Fátima; Duarte, João; Ramos, Sérgio; Vale, ZitaA definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.
- Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsamplingPublication . Duarte, Jorge; Fred, Ana; Rodrigues, Fátima; Duarte, João; Ramos, Sérgio; Vale, ZitaWith the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.
- Specificities of thermalism in health tourism: the mediating role of the territoryPublication . Brandão, Filipa; Liberato, Dália; Duarte, João; Liberato, Pedro; Quintela, Joana A.Wellness is recognized in several research studies as a common term regarding health, quality of life and satisfaction, in association with or replacing wellness. Also, the concept of quality of life is important to complement health tourism. Quality of life is useful in conceptualizing well-being by establishing links between objective and subjective features of this concept. In the search for a better quality of life, individuals look for ways to improve their well-being. In this sense and in the framework of this work, thermalism as a health activity arises as one of the most natural ways to improve well-being. Thermalism is defined as the use of natural mineral water and other complementary methods for prevention, therapy, rehabilitation or wellness. This research proposes a development strategy for thermal springs in the North of Portugal through the identification of the currently existing health and wellness, tourism, cultural and heritage attributes in the region. A qualitative methodology is applied, through semi-structured interviews, having as objectives to contextualize thermal tourism in health tourism and to identify the specialized thermal services available in the thermal regions of Northern Portugal. The results show the existing difficulties in the sector, namely the marked seasonality of demand and human resources, the non-diversity of age among thermal users, the lack of innovative equipment, the impact of the pandemic, the restrictions imposed on the thermal operation during the pandemic, the lack of tourist support structures, the scarcity of available and quality accommodation, the missing municipal and/or regional support, which permanently challenge the sector. In what concerns the identification of the specialized thermal services available in the thermal regions of Northern Portugal, it emerges the availability of opening services for the school community, free transportation and reception of local users in a thermal environment, specific thermal circuits; thermal massages, and differentiating treatments by the mineral-medicinal properties of the thermal waters of each geographical location.
- Typical load profiles in the smart grid context – a clustering methods comparisonPublication . Ramos, Sérgio; Duarte, João; Soares, João; Vale, Zita; Duarte, JorgeThe present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.