Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-08-29T18:46:25Z No. of bitstreams: 1 HARLLAN ANDRYÊ BEZERRA SILVA – DISSERTAÇÃO (PPGCC) 2017.pdf: 2631344 bytes, checksum: beb0769167a696c9f8afd54cf5b99127 (MD5) / Made available in DSpace on 2018-08-29T18:46:25Z (GMT). No. of bitstreams: 1 HARLLAN ANDRYÊ BEZERRA SILVA – DISSERTAÇÃO (PPGCC) 2017.pdf: 2631344 bytes, checksum: beb0769167a696c9f8afd54cf5b99127 (MD5) Previous issue date: 2017-05-12 / O consumo de energia elétrica vem crescendo a cada dia. Precisamos utilizar a energia elétrica de forma consciente, pois os recursos naturais que são utilizados para a geração de energia podem acabar devido ao seu uso ineficiente. O crescimento populacional das últimas décadas, o aparecimento de mais aparelhos eletrônicos e eletrodomésticos geram um consumo excessivo de energia. Devido ao crescimento no consumo de energia elétrica é necessária a implantação de programas de eficiência energética, que se dá através da introdução de novas tecnologias, incentivo à mudança de hábito do próprio consumidor e uso racional de energia elétrica. O foco deste trabalho é no setor residencial, que é o segundo maior consumidor de energia elétrica no Brasil, e como há consumidores que compartilham características e padrões de carga semelhantes, isso possibilita o uso de agrupamento de dados. Pensando nisso é proposto o uso de agrupamento para auxiliar programas de eficiência energética na análise dos dados dos consumidores e na criação de grupos representativos de uma população. A criação de grupos ajuda a concessionária de energia a fornecer ofertas comerciais ou recomendações específicas para grupos específicos, diminuir a complexidade das análises que teriam que ser feitas em uma população e obter relacionamentos personalizados, mais eficazes e equitativos entre os fornecedores de energia e seus clientes. O agrupamento irá proporcionar a aplicação de soluções que ajudem o consumidor a utilizar energia elétrica de forma eficiente, a partir do momento em que ele recebe informações sobre seu consumo e como ele poderá utilizar essas informações, sabendo o que elas irão proporcionar como resultado. Este trabalho iniciou-se com a investigação de medidas de dissimilaridade para representar a semelhança entre perfis de consumo de energia elétrica (um dos fatores utilizados para os agrupamentos) e entre as três medidas utilizadas a distância Euclidiana se destacou com os melhores resultados nos experimentos feitos, seja variando a quantidade de observações das séries ou a base de dados. Após isso foram feitos agrupamentos utilizando 4 fatores extraídos da base de dados e assim criados 15 cenários de agrupamentos a partir da combinação desses fatores. Por meio dos resultados desses agrupamentos foi possível reduzir a quantidade de cenários por serem semelhantes e também escolher os cenários (fatores) mais relevantes a serem considerados quando se quer criar grupos de consumidores residenciais. / The consumption of electric energy has been increasing every day. We need to use electric power in a conscious way, because the natural resources that are used for the generation of energy can end up due to its inefficient use. The population growth of the last decades, the appearance of more electronic devices and appliances generate an excessive consumption of energy. Due to the growth in the consumption of electric energy, it is necessary to implement energy efficiency programs, which are carried out through the introduction of new technologies, an incentive to change the consumer’s habit and rational use of electric energy. The focus of this work is on the residential sector, which is the second largest consumer of electricity in Brazil, and since there are consumers who share similar characteristics and load patterns, this allows the use of data grouping. Thinking about that, the use of clustering to support energy efficiency programs in the analysis of consumer data and in the creation of representative groups of a population is proposed. Groups creation helps the utility to provide commercial offers or specific recommendations for specific groups, reduce the complexity of the analyzes that would have to be done in a population, and get personalized, more effective and equitable relationships between energy suppliers and their customers. The clustering will provide the application of solutions that help the consumer to use electricity efficiently, from the moment he receives information about his consumption and how he can use that information, knowing what they will provide as a result. This work began with the investigation of measures of dissimilarity to represent the similarity between profiles of electric energy consumption (one of the factors used for the clustering) and among the three measures used the Euclidean distance stood out with the best results in the experiments made, either by varying the number of observations of the series or the database. After that, clusters were made using 4 factors extracted from the database and thus 15 clustering scenarios were created from the combination of these factors. Through the results of these clustering it was possible to reduce the number of scenarios to be similar and also to choose the most relevant scenarios to consider when creating groups of residential consumers.

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While the utilization of energy efficiency has grown in recent years, it has not been distributed evenly across the country. In some states, over 2% of a utility’s budget is spent on energy efficiency; in other states that number is 0. Much of the growth in energy efficiency has been due to state policies and the development utility-level energy efficiency programs. Yet, all utility programs are not created equal. Because they are often exempt from state regulation (and therefore state energy efficiency policy), publicly-owned utilities have traditionally lagged behind IOUs when it comes to EE programs. This research quantifies energy efficiency programs in four Midwestern states: Iowa, Indiana, Michigan and Wisconsin. The first part of the thesis evaluates 474 electric utilities as to whether they had an energy efficiency program in 2010. The second part of the thesis evaluates each utility’s EE program spending in terms of energy and utility specific factors, as well as socio-economic, housing stock and political variables. Through descriptive statistical analysis and the creation of a predictable linear regression model, this thesis identifies relationships between the dependent variable (EE program spending as a % of a utility’s total revenue) and commonly cited barriers to EE program development. Through the analysis, this study finds widespread EE program coverage in Iowa, Michigan and Wisconsin. Also, it finds states are the greatest predictor of utility energy efficiency program spending. A utility’s ownership type and the share of homes that heat with electricity are also significant predictors of program spending.