Through theory and econometric instrumentals, this work intends to estimate the behavior of Electric Energy’s Demand Curve to Brazil, through the behavior of important variables for residential sector, industrial and commercial sectors. It’s important to achievement for futures projections that enable to know if installed capacity is enough to answer Energy demand. Using Co-integration concept, were estimated the Vector Error Correction, used to project the demand to the period of 2009-2017. There were some obstacles regarding the estimation of the curve of the industrial sector with respect to the variable IPA-OG industrial machinery and equipment and IPA-OG fuels and lubricants, where their estimated coefficients were inconclusive. Energy consumption by the residential sector is showing increasing but at a decreasing rate, and was influenced by the efficiency of consumption (consumer behavior on the part of the post-rationing and the use of more efficient equipment for the use of electricity). As results, the Energy’s demand is growing and may be superior to installed capacity. Was introduced the Social Cost Politic concept to Energy’s demand and the results show that it politics can reduce the demand, and it can be used as a restrictive measure to Energy’s demand, rather than aggressive restrictive politics. / Com base em subsídios teóricos e no instrumental econométrico, este trabalho pretende estimar o comportamento da Curva de Demanda por energia elétrica para o Brasil, considerando o comportamento de variáveis relevante aos setores residencial, comercial e industrial. A estimação da curva de demanda propicia um instrumental que permite a realização de projeções futuras, as quais por sua vez, permitem verificar se a capacidade instalada de energia atende adequadamente a expectativa de demanda por este produto. Pelo conceito de Co-integração, foram estimados os Modelos de Correção de Erros (VEC), utilizado para projetar o consumo para o período de 2009-2017. De forma geral, os resultados alcançados foram bons, não tendo coeficientes não significativos e sendo os sinais dos parâmetros concernentes à teoria econômica. Houve alguns entraves quanto à estimação da curva do setor industrial com relação à variável IPA-OG máquinas e equipamentos industriais e IPA-OG combustíveis e lubrificantes, onde seus coeficientes estimados foram inconclusivos. Utilizou-se, pois, novas proxies para tais variáveis, a saber, câmbio real efetivo e preço médio – óleo combustível. Através dos resultados alcançados, o consumo crescente de energia pode ser superior a capacidade de fornecimento de energia, evidenciando a necessidade de implantação de políticas restritivas ao consumo de energia. O consumo de energia por parte do setor residencial se mostra crescente, mas a uma taxa decrescente, sendo influenciado pela maior eficiência do consumo (comportamento por parte do consumidor pós-racionamento e utilização de equipamentos mais eficientes quanto ao uso da energia elétrica). Buscou-se introduzir o conceito de Custo Social ao consumo de energia e os resultados mostram que dentre os setores, o industrial sofrera maior penalidade no consumo e que tal política pode proporcionar redução no consumo, podendo ser esta política utilizada como medida restritiva para a demanda por energia.

Made available in DSpace on 2010-04-20T20:51:44Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-03-10T00:00:00Z / Esta pesquisa teve como principal objetivo examinar a relação entre Consumo de Energia Elétrica e Renda Familiar nos domicílios do município de São Paulo. Investigou-se a utilidade do consumo de energia elétrica como base para um indicador que possibilite a extensão e o refinamento do Critério de Classificação Econômica Brasil para estimar o poder de compra da população em geral. A pesquisa dividiu-se em dois níveis de investigação. O primeiro, domiciliar, para o qual foram utilizados três conjuntos de dados oriundos de pesquisas domiciliares (Pesquisa ABRADEE, Pesquisa de Posses e Hábitos do PROCEL, e Pesquisa de Microcrédito da Baixa Renda da FGV-EAESP). O segundo nível, territorial, investigou indicadores de renda, consumo de energia elétrica e classe econômica agregados por áreas de ponderação (conjunto de setores censitários), e utilizou microdados do Censo Demográfico 2000 do município de São Paulo em conjunto com a base de domicílios da AES Eletropaulo. A investigação domiciliar mostrou que não há vantagens na substituição plena da aplicação do Critério Brasil pela coleta de indicadores de consumo de energia elétrica em levantamentos domiciliares. No entanto, o uso combinado do Critério Brasil, do valor da conta de luz e do número de pessoas (ou número de dormitórios) no domicílio apresenta benefícios na classificação da renda (os gráficos de ganhos das árvores de classificação combinadas aproximam-se mais da distribuição real da renda, apesar de o coeficiente de explicação da renda aumentar apenas de 0,577 para 0,582). Além disso, ao contrário do que se especulava, para a baixa renda a associação entre renda e consumo de energia elétrica mostrou-se fraca, apesar de o coeficiente de explicação da renda aumentar de 0,222 para 0,300 quando incorporamos o consumo de energia elétrica e o número de pessoas ao modelo de regressão da renda pelo Critério Brasil. Em nível territorial, as relações entre Renda, Consumo de Energia Elétrica e Classificação Econômica do Critério Brasil mostraram-se muito fortes (os coeficientes de explicação da renda atingiram valores de 0,912 a 0,960), permitindo que medidas de consumo médio de energia elétrica agregadas em áreas de ponderação sejam ótimos indicadores regionais de concentração de renda e classificação econômica dos domicílios para o município de São Paulo. Por serem atuais, disponíveis e de atualização mensal, os indicadores de consumo de energia elétrica, quando disponibilizados pelas empresas de distribuição de energia, podem ser de grande utilidade para empresas de mercado, como subsídio a estratégias que necessitem de informações de classificação, concentração e previsão da renda domiciliar. / The main research objective was to analyze the relationship between household Electricity Consumption and Family Income in the city of São Paulo in order to evaluate the potential benefits of adding electricity consumption to the Brazilian Economic Classification Criteria, an index frequently used to estimate a family’s purchasing power. To achieve these goals, statistical models were developed at two different levels of aggregation. The first and most disaggregated level was the household, for which data from three different research studies were combined (ABRADEE Research, PROCEL Possessions and Habits Research, and Microcredit for Low Income Families Research by FGV-EAESP). The second level (the territorial one), with household aggregated at the level of weighted areas (set of census tracts), used income indicators, electric energy consumption and economic classification data from the Demographic Census 2000 of the city of São Paulo and the AES Eletropaulo household databases. The results in household investigation show that energy consumption alone cannot substitute for Brazilian Criteria. However, the combined use of the Brazilian Criteria, the household electricity monthly bill and the number of residents (or number of bedrooms) in the household significantly improves household income estimates, as shown by the results of classification tress model, in which the resulting predicted distribution curve better approximates the real distribution, although coefficient of determination R-squared grows from only 0,577 to 0,582. However, contrary to a priori expectation, among low income household, the level of association between income and electricity consumption was very weak. Nevertheless, household income forecast can be enhanced, with an improvement of the model’s R-squared from 0,222 to 0,300 when the electricity bill and the number of resident are included in a regression model of household income against the Brazilian Criteria. At the weighted area level, the relationship between Income, Electricity Consumption and Brazilian Criteria’s Economic Classification are very strong, with coefficients of determination R-squared ranging from 0,912 to 0,960. This results supports the use of the mean household electricity consumption, at a territorial aggregated level, as an excellent regional indicator of income concentration in the city of São Paulo. As it is an easily available and monthly update information, the electric energy consumption indicators, if made available by energy distribution companies, will be useful for strategy formulation and decision making for which household income classification data are critical.

This research studies the relationships in Electric Energy Consumption, Carbon Dioxide Emission and Theil Index in Brazil. The period of analysis includes annual data from 1980 to 2011 in a total of 31 observations. The series presented order of integration equal one with the presence of cointegration thus to measure these influences we used a vector error correction model (VEC). By Function Impulse Response (FIR) and Variance Decomposition Analysis (ADV) we observed how each variable behaves to an abrupt change. To analyze the behavior of variables, methods of vector autoregressive (VAR) and residues control charts were used. The VAR modeling revealed that there is a significant interrelationship among the variables under study, thus showing that there is a short-term relationship between these variables. As for the residues control chart to individual measures, a problem in the original variables was avoided tha were the the autocorrelation, and showed that all variables had a period of instability and also enabled the identification of this period. The emission of carbon dioxide and Theil Index are determining factors in the explanation of environmental impacts as well as the development of the country. The variance decomposition indicates that the carbon dioxide emission is primarily responsible for mainly caused damage to the environment. / Esta pesquisa estudou as relações existentes no Consumo de Energia Elétrica, Emissão de Dióxido de Carbono e Índice de Theil no Brasil. O período de análise se refere a dados anuais de 1980 a 2011 perfazendo um total de 31 observações do Brasil. As séries apresentaram ordem de integração igual a um com a presença de cointegração, assim, para mensurar essas influências foi utilizado um modelo de Vetor de Correção de Erros (VEC). Por meio da Função Impulso Resposta (FIR) e Análise de Decomposição da Variância (ADV) foi possível verificar como cada variável se comporta a uma mudança abrupta. Para analisar o comportamento das variáveis, foram utilizadas as metodologias de vetores auto regressivos (VAR) e gráficos de controle de resíduos. Já a modelagem VAR revelou que há um inter-relacionamento significativo entre as variáveis em estudo, mostrando assim que há uma relação de curto prazo entre estas variáveis. Quanto aos gráficos de controle de medidas individuais aos resíduos, contornou-se um problema presente nas variáveis originais que era o de autocorrelação, e mostrou-se que todas as variáveis apresentaram um período de instabilidade o que também possibilitou a identificação deste período. A Emissão de Dióxido de Carbono e o Índice de Theil são fatores determinantes na explicação dos impactos ambientais, assim como no desenvolvimento do país. A decomposição da variância indica que a Emissão de Dióxido de Carbono é o principal responsável pelos danos causados principalmente ao meio ambiente.

Submitted by Antonio Alves Souza Filho (aafilho@policiamilitar.sp.gov.br) on 2018-10-06T01:47:13Z No. of bitstreams: 1 Souza Filho. AA 2018 (rev 4).pdf: 4649855 bytes, checksum: dcfc50a9345ca994092558164184fe17 (MD5) / Approved for entry into archive by Tamara Oliveira (tamara.oliveira@fgv.br) on 2018-10-09T16:31:16Z (GMT) No. of bitstreams: 1 Souza Filho. AA 2018 (rev 4).pdf: 4649855 bytes, checksum: dcfc50a9345ca994092558164184fe17 (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-10-10T12:22:55Z (GMT) No. of bitstreams: 1 Souza Filho. AA 2018 (rev 4).pdf: 4649855 bytes, checksum: dcfc50a9345ca994092558164184fe17 (MD5) / Made available in DSpace on 2018-10-10T12:22:55Z (GMT). No. of bitstreams: 1 Souza Filho. AA 2018 (rev 4).pdf: 4649855 bytes, checksum: dcfc50a9345ca994092558164184fe17 (MD5) Previous issue date: 2018-08-28 / No mundo corporativo o aumento da lucratividade permeia por ações efetivas em relação à prevenção e a redução de desperdícios. Em 2016 os dados da Empresa de Pesquisa Energética - EPE mostraram que o setor público apresentou taxa de crescimento do consumo de energia elétrica de 2,3% ao ano em média em 20 anos, sendo equivalente a taxa da indústria, porém sem que o setor público realize a manufatura de qualquer produto e, acima, ainda, dos setores Residencial e Agropecuário no mesmo período, exacerbando a questão sobre a eficiência na gestão dos recursos financeiros no setor público e levando a questionar qual o nível de desperdício existente. O presente trabalho objetivou analisar tecnologias como meio para tornar prédios públicos utilizados pela Polícia Militar do Estado de São Paulo - PMESP mais sustentáveis, direcionada a entregar os resultados com melhor eficiência nos consumos de água e energia, minimizando desperdícios, utilizando-se a metodologia qualitativa com um estudo de caso, revisão bibliográfica e aplicação de pesquisa estruturada, de modo a compreender quais as barreiras e alavancas para a implantação de tecnologias ambientais em prédios públicos no Estado de São Paulo, mantendo suas atividades rotineiras, com gestão mais eficiente, direcionada nos consumos de energia e de água. A Escola Superior de Sargentos – ESSgt, situada na Avenida Condessa Elizabeth de Robiano, 750, São Paulo-SP, foi selecionada para este estudo de caso, visto ter sido pioneira na certificação ambiental e de qualidade, propiciando um ambiente de maior aderência a este estudo e no estabelecimento de padrões com diretrizes para licitação das tecnologias, difundindo a outros edifícios públicos. Referente aos ganhos ambientais, concluiu-se que a eficiência energética e o uso mais eficiente do recurso hídrico observados podem assegurar retorno econômico e ecoeficiente. / In a corporate world the increase in profitability permeates by effective actions in relation to the prevention and reduction of waste. In 2016 the Energy Research Company (Empresa de Pesquisa Energética) - EPE data showed that the public sector presented an average energy consumption annual growth rate of 2.3% per year in 20 years, being equivalent to the industry rate, but without the public sector realizing the manufacture of any product and, above, still, the residential and agricultural sectors in the same period, intensifying the issue of efficiency in the management of financial resources in the public sector and leading to question the level of waste. The objective of this study is based on analyze technologies as a means to make public buildings used by the São Paulo Military Police Corporation - PMESP more sustainable, aimed at delivering the results with better efficiency in the consumption of water and energy, minimizing waste, using qualitative methodology with some case study, bibliographical review and structured research application, in order to understand the barriers and lever for the implantation of environmental technologies in public buildings in São Paulo State , maintaining its routine activities, with management more efficiency, focused on energy and water consumptions. The Senior Sergeant School - ESSgt (Escola Superior de Sargentos), located at 750, Condessa Elizabeth de Robiano Avenue, São Paulo-SP, in Brazil, was elected for this case study in particular, because it was a pioneer in environmental and quality certification, providing an environment of greater adherence to this study and the establishment of standards with guidelines for the bidding of the technologies, spreading to other public buildings. Regarding the environmental gains, it was concluded that the energy efficiency and the more efficient use of the water resource observed, can ensure economic and eco-efficient return.

Der Einsatz von erneuerbare Energien und die Anwendung von Energieeffizienz spielt derzeit in den GCC-Staaten noch eine untergeordnete Rolle, aber das Interesse daran ist in den letzten Jahren gewachsen. Die Arbeit wird die Chancen und Hindernisse für den Einsatz von erneuerbare Energien, Empfehlungen für eine bessere Energieeffizienz und für eine stärkere Integration der RE in den Energiemix der GCC-Staaten präsentieren. Die Arbeit beschreibt die traditionelle Energiesituation und analysiert die technischen, wirtschaftlichen und ökologischen Gesichtspunkte des Energieverbrauches in den GCC-Ländern. Der Fokus liegt auf Anwendungen der Solarenergie und Energieeffizienz, jedoch wird auch kurz auf Windenergie, Biomasse und einige Lösungen für energieeffizientes Bauen eingegangen. Außerdem werden Vergleiche mit der Situation in Europa und der Welt vorgenommen. Es soll erreicht werden, dass der Energieverbrauch reduziert sowie Energieeffizienz und die Nutzung erneuerbare Energien gefördert werden.

The Industrial Internet of Things (IIoT) is growing day by day and is implemented in many industries. The centralized architecture of an IIoT system is composed of several devices that communicate with a special device only via one link, in an instance where this one link is attacked, major problems could occur for the whole system. The solution is to decentralize the entire architecture, a feature that the implementation of blockchain technology provides. Blockchain technology uses numerous consensus algorithms and some of the consensus algorithms require a large amount of computational power , such as the proof of work consensus algorithm. The problem is that IIoT devices have limited processor performance therefore it is important to find consensus algorithms that are suitable for an IIoT system in terms of time efficiency and electricity consumption. The question then becomes, which of the following different consensus algorithms; proof of work, proof of stake and proof of authentication performs best in an IIoT environment in terms of time efficiency and electricity consumption?  This question can be answered by implementing blockchain technology using the three aforementioned consensus algorithms in an IIoT environment to see which consensus algorithm is the most time efficient and uses the smallest amount of electricity. The results showed that proof of stake was the best consensus algorithm both in terms of time efficiency and electricity consumption. / Sakernas internet inom industrin (IIoT) växer dag för dag och används i flertalet industrier. Den centraliserade arkitekturen av ett IIoT-system består av flera enheter som kommunicerar med en speciell enhet endast via en länk och detta kan skapa stora problem för hela systemet om endast denna länk attackeras. Lösningen är att decentralisera hela arkitekturen, en funktion som implementeringen av blockkedjeteknologi förser. Inom blockkedjeknologi används flertalet algoritmer och bland algoritmerna finns det flera som kräver hög processorprestanda, som t.ex proof of work algoritmen. Problemet är att IIoT-enheter har begränsad processorprestanda, och ett viktigt skäl är att hitta algoritmer som är anpassade för ett IIoT-system beträffande tidseffektivitet samt elkonsumtion. Frågan blir då, vilken av de olika konsensus algoritmerna; proof of work, proof of stake och proof of authentication presterar bäst i en IIoT-miljö sett ur tidseffektivitet och elkonsumtion?  Denna fråga kan besvaras genom att implementera blockkedjeteknologi med de tre ovannämnda algoritmer i en IIoT-miljö för att se vilken algoritm är den mest tidseffektiva och har lägst elkonsumtion. Resultatet visade att proof of stake var den bästa konsensus algoritmen både tidsmässigt och elkonsumtion mässigt.

Idag är det möjligt att koppla upp utrustningar mot nätet hemma och ha ett Smart Hem. Ett hushåll har två elavtal, elnät och elhandel. Elnät är bunden till området man bor och kan inte bytas ut, men det är annorlunda med elhandel. Hos elhandel vill man ha det lägsta priset på el och det är där timpris spelar roll. Elkostnader kan bli höga och för att minska kostnaderna samt använda grön energi har detta examensprojekt tagit fram ett system som ska kunna informera en användare om spotpriset respektive väder. Projektet har använt sig av Android Studio (front-end), Firebase Realtime database och ett javaprogram (back-end). Med Grönt Väders API och Google Maps Geocoding API har data om användaren, elpriser och väder samlats i databasen. Resultatet av projektet blev en mobilapplikation som visar information om dagens elpris, väder och andra faktorer som hjälper ett hushåll med egenproducerad energi att följa. Dock kunde elpriser inte jämföras med vädret. För vidareutveckling hade back-end kunnat vara på en Raspberry Pi där man kopplar en vitvara som man kan styra i framtiden. / Today it is possible to connect equipment to the network at home and have a Smart Home. A household has two electricity agreements, electricity networks and electricity trading. Electricity network is tied to the area you live and cannot be changed, but electricity trading is a different matter. In electricity trading, what you want is the lowest price and that is where the hourly rate matters. Electricity costs can be high and to reduce costs and use green energy, this degree project has developed a system that will inform a user about the spot price and weather. The project has used Android Studio (front-end), Firebase Realtime database and a Java program (back-end). With the Green Weather API and the Google Maps Geocoding API has information about the user, electricity prices and weather been collected in the database. The results in this project was an app that showed electricity prices of the day, weather and other factors that helps a household with self-produced energy to follow. However, electricity prices could not be compared with weather. Further development would be to have the back-end running on a Raspberry Pi where appliances could be controlled in the future.

Digitization of the energy industry, introduction of smart grids and increasing regulation of electricity consumption metering have resulted in vast amounts of electricity data. This data presents a unique opportunity to understand the electricity usage and to make it more efficient, reducing electricity consumption and carbon emissions. An important initial step in analyzing the data is to identify anomalies. In this thesis the problem of anomaly detection in electricity consumption series is addressed using four machine learning methods: density based spatial clustering for applications with noise (DBSCAN), local outlier factor (LOF), isolation forest (iForest) and one-class support vector machine (OC-SVM). In order to evaluate the methods synthetic anomalies were introduced to the electricity consumption series and the methods were then evaluated for the two anomaly types point anomaly and collective anomaly. In addition to electricity consumption data, features describing the prior consumption, outdoor temperature and date-time properties were included in the models. Results indicate that the addition of the temperature feature and the lag features generally impaired anomaly detection performance, while the inclusion of date-time features improved it. Of the four methods, OC-SVM was found to perform the best at detecting point anomalies, while LOF performed the best at detecting collective anomalies. In an attempt to improve the models' detection power the electricity consumption series were de-trended and de-seasonalized and the same experiments were carried out. The models did not perform better on the decomposed series than on the non-decomposed. / Digitaliseringen av elbranschen, införandet av smarta nät samt ökad reglering av elmätning har resulterat i stora mängder eldata. Denna data skapar en unik möjlighet att analysera och förstå fastigheters elförbrukning för att kunna effektivisera den. Ett viktigt inledande steg i analysen av denna data är att identifiera möjliga anomalier. I denna uppsats testas fyra olika maskininlärningsmetoder för detektering av anomalier i elförbrukningsserier: densitetsbaserad spatiell klustring för applikationer med brus (DBSCAN), lokal avvikelse-faktor (LOF), isoleringsskog (iForest) och en-klass stödvektormaskin (OC-SVM). För att kunna utvärdera metoderna infördes syntetiska anomalier i elförbrukningsserierna och de fyra metoderna utvärderades därefter för de två anomalityperna punktanomali och gruppanomali. Utöver elförbrukningsdatan inkluderades även variabler som beskriver tidigare elförbrukning, utomhustemperatur och tidsegenskaper i modellerna. Resultaten tyder på att tillägget av temperaturvariabeln och lag-variablerna i allmänhet försämrade modellernas prestanda, medan införandet av tidsvariablerna förbättrade den. Av de fyra metoderna visade sig OC-SVM vara bäst på att detektera punktanomalier medan LOF var bäst på att detektera gruppanomalier. I ett försök att förbättra modellernas detekteringsförmåga utfördes samma experiment efter att elförbrukningsserierna trend- och säsongsrensats. Modellerna presterade inte bättre på de rensade serierna än på de icke-rensade.

No description available.

This pilot study consists of two parts. The first part investigates the extent to which the domestic electricity consumers intend to use and use energy efficiently using the Theory of Planned Behaviour. The second part investigates the extent to which the Energy @ Home educational intervention changed the domestic electricity consumers’ behaviour. For the first part of the study an advertisement was published and a convenience stratified sample of 61 domestic electricity consumers were selected from the 290 respondents. Data was collected from the domestic electricity consumers via a questionnaire and a telephone response log. The co-relational research design was used to investigate the relationship between the predictor variables the independent variables in the constructs of the Theory of Planned Behaviour. Simple linear regression analysis resulted in F statistic for the predicted behavioural intention was 29.74 with a p value less than 0.0001 which indicates significant statistical evidence of a linear relation between the predictor variables and the independent variables. The r2 of 0.87 implies that data points that fall closely along the best fit line. Therefore the predictor variables were good predictors of the response variable. All the participants that intended to use electricity efficiently confirmed via the telephone that they were using electricity efficiently. In the second part of the study 11 out of the 61 participants were chosen to participate in the Energy @ Home educational intervention and television program. Data was collected via the Energy audit log and the electricity consumption log. The participants intended to save between 2% and 35% of their electricity consumption and the actual electricity consumption savings were between 2% and 30%. / Science and Technology Education / M. Sc. (Mathematics, Physics & Technology Education (Physics Education))