Os benefícios e as dificuldades na certificação da qualidade: Norma NP EN ISO 9001:2008

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do Grau de Mestre em Assessoria de Administração

Qualidade, higiene e segurança na gestão das empresas

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração para obtenção do Grau de Mestre em Auditoria. Orientada por: Mestre Alcina Dias

Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With 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.

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.

Intelligent management of end consumers loads including electric vehicles through a SCADA system

The large penetration of intermittent resources, such as solar and wind generation, involves the use of storage systems in order to improve power system operation. Electric Vehicles (EVs) with gridable capability (V2G) can operate as a means for storing energy. This paper proposes an algorithm to be included in a SCADA (Supervisory Control and Data Acquisition) system, which performs an intelligent management of three types of consumers: domestic, commercial and industrial, that includes the joint management of loads and the charge/discharge of EVs batteries. The proposed methodology has been implemented in a SCADA system developed by the authors of this paper – the SCADA House Intelligent Management (SHIM). Any event in the system, such as a Demand Response (DR) event, triggers the use of an optimization algorithm that performs the optimal energy resources scheduling (including loads and EVs), taking into account the priorities of each load defined by the installation users. A case study considering a specific consumer with several loads and EVs is presented in this paper.

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.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

Motivations which influence volunteers' satisfaction

In this paper we attempt to find out which motivations influence volunteers satisfaction. So, in a first moment, we categorize satisfaction through a factor analysis and then use linear regression models to find out the relations previously mentioned. Furthermore we analyse the existence of correlations between some demographic attributes and the other variables. In this research the population under study includes volunteers that work in the health area, more precisely volunteers that work in hospital and have direct contact with patients and their families. We obtained a total of 327 questionnaires and after excluding incomplete answers we get 304 questionnaires which embody a response rate of 36%. The outcomes of our analysis reveal that we can categorize satisfaction into intrinsic and extrinsic categories and show that motivations related to belonging and protection and career recognition are the ones influencing extrinsic satisfaction; motivations associated to development and learning and altruism are the ones with higher effect in intrinsic satisfaction. There are some negative correlations between age and extrinsic satisfaction, between years of participation and extrinsic satisfaction, between education and the motivation related to belonging and protection and between age and the motivation related to career recognition. There is only one positive correlation between hours per week and intrinsic satisfaction. The results offer new insights for research about volunteers’ motivations, motive fulfillment and volunteer satisfaction. Furthermore the outcomes advocate that to ensure satisfied volunteers, their motivations must be identified in a timely and appropriate moment, meaning that should happened as early as possible.

Organisational influence on volunteer satisfaction and attitudes towards HRM practices: the case of hospital volunteers

We examine volunteer satisfaction with HRM practices, namely recruitment, training and reward in NPOs and attitudes regarding the appropriateness of these practices. The participants in this study are 76 volunteers affiliated with four different NPOs, who work in hospitals and have direct contact with patients and their families. Analysing aggregate results we show that volunteers are more satisfied with training, and consider the training strategies to be very appropriate. After identifying differences between organisations we discover that in some organisations volunteers are satisfied with rewards but they have negative attitudes regarding the appropriateness of the recognition strategies. We also identify the volunteers who are the most and the least satisfied.

Learner’s satisfaction within a breast imaging eLearning course for radiographers

Background: An asynchronous eLearning system was developed for radiographers in order to promote a better knowledge about senology and mammography. Objectives: to assess the learners’ satisfaction. Methods: Target population included radiographers and radiogr aphy students, in order to assess eLearning satisfaction according to different experience levels in breast imaging. Satisfaction was measured through a questionnaire developed especially for eLearning systems, using a seven - point Likert scale. Main topics related are content, interface, personalization and learning community. Results: Overall, 85% of learners were satisfied with the course and 87,5% considered that the course is successful. Main areas that were evaluated by most learners in a positive way were interface and content (between six and seven - point); on the other hand, learning community presented a wider distribution of answers . Conclusions: The course provides an overall high degree of learner satisfaction, thus providing more effective knowle dge gain on breast imaging for radiographers.

Imagem da loja: estudo empírico de uma loja de ótica

Dissertação Apresentada ao Instituto Politécnico do Porto para obtenção do Grau de Mestre em Gestão das Organizações, Ramo Gestão de Empresas Orientada pelo Professor Doutor José de Freitas Santos

Impact of Social Responsibility Programmes in Stakeholder Satisfaction: An Empirical Study of Portuguese Managers’ Perceptions

Investigação no âmbito do Projeto de Doutoramento(PhD), especialidade de Gestão Global, Estratégia e Desenvolvimento Empresarial (ISCTE-IUL - 2012, classificação de “Aprovado com Muito Bom”).

SCADA House Intelligent Management for Energy Efficiency Analysis in Domestic Consumers

The implementation of smart homes allows the domestic consumer to be an active player in the context of the Smart Grid (SG). This paper presents an intelligent house management system that is being developed by the authors to manage, in real time, the power consumption, the micro generation system, the charge and discharge of the electric or plug-in hybrid vehicles, and the participation in Demand Response (DR) programs. The paper proposes a method for the energy efficiency analysis of a domestic consumer using the SCADA House Intelligent Management (SHIM) system. The main goal of the present paper is to demonstrate the economic benefits of the implemented method. The case study considers the consumption data of some real cases of Portuguese house consumption over 30 days of June of 2012, the Portuguese real energy price, the implementation of the power limits at different times of the day and the economic benefits analysis.

Defining Electricity Tariffs Using the Knowledge About the Consumers Profiles in ELECON Project

The increasing importance of the integration of distributed generation and demand response in the power systems operation and planning, namely at lower voltage levels of distribution networks and in the competitive environment of electricity markets, leads us to the concept of smart grids. In both traditional and smart grid operation, non-technical losses are a great economic concern, which can be addressed. In this context, the ELECON project addresses the use of demand response contributions to the identification of non-technical losses. The present paper proposes a methodology to be used by Virtual Power Players (VPPs), which are entities able to aggregate distributed small-size resources, aiming to define the best electricity tariffs for several, clusters of consumers. A case study based on real consumption data demonstrates the application of the proposed methodology.

Consumers Performance Evaluation of the Participation in Demand Response Programs Using Baseline Methods

Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.