European Union has set a target to install nearly 200 million smart metersspread over Europe before 2020, this leads into a vast increase of sensitiveinformation flow for Distribution System Operators (DSO’s), simultaneously thisleads to raised cyber security threats. The in and outgoing information of the DSOneeds to be processed and stored by different Information technology (IT)- andOperational Technology (OT)-systems depending on the information. High demandsare therefore required of the enterprise cyber security to be able to protect theenterprise IT- and OT-systems. Sensitive customer information and a variety ofservices and functionality is examples that could be fatal to a DSO if compromised.For instance, if someone with bad intentions has the possibility to tinker with yourelectricity, while you’re away on holiday. If they succeed with the attack and shuttingdown the house electricity, your food stored in your fridge and freezer would mostlikely to be rotted, additionally damage from defrost water leaking could cause severedamaging on walls and floors. In this thesis, a detailed reference model of theadvanced metering architecture (AMI) has been produced to support enterprisesinvolved in the process of implementing smart meter architecture and to adapt to newrequirements regarding cyber security. This has been conduct using foreseeti's toolsecuriCAD, foreseeti is a proactive cyber security company using architecturemanagement. SecuriCAD is a modeling tool that can conduct cyber security analysis,where the user can see how long time it would take for a professional penetrationtester to penetrate the systems in the model depending of the set up and defenseattributes of the architecture. By varying defense mechanisms of the systems, fourscenarios have been defined and used to formulate recommendations based oncalculations of the advanced meter architecture. Recommendation in brief: Use smalland distinct network zones with strict communication rules between them. Do diligentsecurity arrangements for the system administrator PC. The usage of IntrusionProtection System (IPS) in the right fashion can delay the attacker with a percentageof 46% or greater. / Europeiska Unionen har satt upp ett mål att installera nära 200miljoner smarta elmätare innan år 2020, spritt utöver Europa, implementeringen ledertill en rejäl ökning av känsliga dataflöden för El-distributörer och intresset av cyberattacker ökar. Både ingående och utgående information behöver processas och lagraspå olika IT- och OT-system beroende på informationen. Höga krav gällande ITsäkerhet ställs för att skydda till exempel känslig kundinformation samt en mängdvarierande tjänster och funktioner som är implementerade i systemen. Typer avattacker är till exempel om någon lyckats få kontroll over eltillgängligheten och skullestänga av elektriciteten till hushåll vilket skulle till exempel leda till allvarligafuktskador till följd av läckage från frysen. I den här uppsatsen så har en tillräckligtdetaljerad referens modell för smart elmätar arkitektur tagits fram för att möjliggörasäkerhetsanalyser och för att underlätta för företag i en potentiell implementation avsmart elmätare arkitektur. Ett verktyg som heter securiCAD som är utvecklat avforeseeti har använts för att modellera arkitekturen. securiCAD är ett modelleringsverktyg som använder sig av avancerade beräknings algoritmer för beräkna hur långtid det skulle ta för en professionell penetrationstestare att lyckats penetrera de olikasystem med olika sorters attacker beroende på försvarsmekanismer och hurarkitekturen är uppbyggd. Genom att variera systemens försvar och processer så harfyra scenarion definierats. Med hjälp av resultaten av de fyra scenarierna så harrekommendationer tagits fram. Rekommendationer i korthet: Använd små ochdistinkta nätverkszoner med tydliga regler som till exempel vilka system som fårkommunicera med varandra och vilket håll som kommunikationen är tillåten.Noggranna säkerhetsåtgärder hos systemadministratörens dator. Användningen avIPS: er, genom att placera och använda IPS: er på rätt sätt så kan man fördröjaattacker med mer än 46% enligt jämförelser mellan de olika scenarier.

This thesis presents the development of techniques which enable appliance recognition in an Advanced Electricity Meter (AEM) to aid individuals reduce their domestic electricity consumption. The key aspect is to provide immediate and disaggregated information, down to appliance level, from a single point of measurement. Three sets of features including the short term time domain, time dependent finite state machine behaviour and time of day are identified by monitoring step changes in the power consumption of the home. Associated with each feature set is a membership which depicts the amount to which that feature set is representative of a particular appliance. These memberships are combined in a novel framework to effectively identify individual appliance state changes and hence appliance energy consumption. An innovative mechanism is developed for generating short term time domain memberships. Hierarchical and nearest neighbour clustering is used to train the AEM by generating appliance prototypes which contain an indication of typical parameters. From these prototypes probabilistic fuzzy memberships and possibilistic fuzzy typicalities are calculated for new data points which correspond to appliance state changes. These values are combined in a weighted geometric mean to produce novel memberships which are determined to be appropriate for the domestic model. A voltage independent feature space in the short term time domain is developed based on a model of the appliance’s electrical interface. The components within that interface are calculated and these, along with an indication of the appropriate model, form a novel feature set which is used to represent appliances. The techniques developed are verified with real data and are 99.8% accurate in a laboratory based classification in the short term time domain. The work presented in this thesis demonstrates the ability of the AEM to accurately track the energy consumption of individual appliances.

The diploma thesis provides an insight into the remote control and disconnection of DSO supply points phenomenon. The remote or local disconnection/connection of supply point is allowed by the breaker function. The automatic disconnection of supply point is enabled by the limiter function. Due to the anticipated implementation of smart meters in the Czech Republic in the future, this work contains the comprehensive description of breaker/limiter function with proposed possibilities of use in the Czech Republic. The thesis deals with the current breaker/limiter function use in the Czech Republic and in other countries. It introduces the smart meter installation in pilot projects to analyze the breaker/limiter function use in other countries. The thesis is focused on the technical solution of breaker/limiter. Moreover, it describes the ways of connecting the breaker, settting the limiter, connecting/disconnecting a supply point and breaker operation. Further, the thesis introduces the ways of activating the breaker by a customer and defines in which cases it is possible to limit and interrupt the electricity supply in the Czech Republic currently. The main aim of thesis is to describe the specific possibilities of breaker/limiter function use in the Czech Republic. With regard to the function use in other countries and the limiting or interrupting the electricity supply by DSO according to energy law, the possibilities of use are proposed. Each possibility of use is analyzed when implementing the breaker function or the breaker/limiter function. The benefits are defined for a DSO and for a customer. The proposed uses are evaluated in terms of applicability and valid legislation in the Czech Republic. The result of this work is the summary of information about breaker/limiter function which is one of the new features in the implementation of smart metering. The function installation and the implementation of possibilities described in the thesis depends on the DSO decision.

Buildings account for around 40 percent of the primary energy consumption in Europe and in the United States. They also hold tremendous energy savings potential: 15 to 29 percent by 2020 for the European building stock according to a 2009 study from the European Commission. Verifying and predicting the impact of energy conservation measures in buildings is typically done through energy audits. These audits are costly, time-consuming, and may have high error margins if only limited amounts of data can be collected. The ongoing large-scale roll-out of smart meters and wireless sensor networks in buildings gives us access to unprecedented amounts of data to track energy consumption, environmental factors and building operation. This Thesis explores the possibility of using this data to verify and predict the impact of energy conservation measures, replacing energy audits with analytical software. We look at statistical analysis techniques and optimization algorithms suitable for building two regression models: one that maps environmental (e.g.: outdoor temperature) and operational factors (e.g.: opening hours) to energy consumption in a building, the other that maps building characteristics (e.g.: type of heating system) to regression coefficients obtained from the first model (which are used as energy-efficiency indicators) in a building portfolio. Following guidelines provided in the IPMVP, we then introduce methods for verifying and predicting the savings resulting from the implementation of a conservation measure in a building.

Digitalization is hitting the energy industry by empowering energy producers and retailers, butmore importantly it’s empowering the end customers and the energy producers and retailers areno longer in possession of all power. Due to digitalization, energy networks are beingmodernized and new emerging technologies called smart grids and smart meters have beenintroduced. Smart grids can automatically monitor energy flows and adjust to changes in energysupply and demand. Smart meters on the other hand empowers the consumers to adapt theirenergy usage in both time and volume to different energy prices throughout the day by costcuttingtheir energy.With empowered and conscious end-customers, electricity retailers will have to compete in newways or risk losing their business. There is a risk that the majority of the over 100 electricityretailers in Sweden will be wiped out with time if data and information is not leveraged to theend-customers. This is potentially also threatening the business of TSU as well as othercompanies providing IT solutions to the energy market.For long Tieto Smart Utility (TSU) has offered IT services for both electricity retailers anddistributors across the Nordics. In relation to recently developed solutions as well as theopportunities and challenges created by digitalization and disruptive technologies such as smartmeters, the Nordic countries are in the forefront. Hence, TSU sees a potential in increasing itspresence in Western Europe to provide services to the retailers and distributors. However, inorder to expand to Western European countries TSU seeks to have a greater marketunderstanding of the different markets, in terms of for example market size, market structure,regulations. The problem is to have a structured and comprehensive way to increase marketunderstanding when assessing new West European energy markets due to the major differencesin each country.This thesis therefore aims to develop a framework which enables IT solution providers toconduct a market opportunity analysis in order to increase market awareness and assess theopportunities and potential in Western European markets, influenced by the smart-meter roll outand thus facilitate strategic decisions.A framework has been developed on the foundation on existing frameworks and applied onTSU by conducting a case study on a market opportunity assessment tool for energy IT solutionproviders. The framework consists of three levels of analysis; European-, Country- andCustomer Level which intends to identify market opportunities and potential.This thesis provides a framework for companies who wants to assess market opportunities andfacilitates strategic decisions regarding the potential of entering the markets. The findings of thisthesis has shown that the market opportunities for TSU are the greatest in Germany especiallydue to the market magnitude as well as the status for the smart meter roll-out. Furthermore,since IT solution providers usually offers many different services and solutions, the findings canbe used in a larger extent in order to asses the potential depending on type service and solution.

No description available.

Since the last United Nations Climate Change Conference in 2015 in Paris (the COP 21), world leaders acknowledged climate change. There is no need any more to justify the switch from fossil fuel-based to renewable energy sources. Nevertheless, this transition is far from being straightforward. Besides technologies that are not yet mature -- or at least not always financially viable in today's economy -- the power grid is currently not ready for a rapid and massive integration of renewable energy sources. A main challenge for the power grid is the inadequacy between electric production and consumption that will rise along with the integration of such sources. Indeed, due to their dependence on weather, renewable energy sources are intermittent and difficult to forecast with today's tools. As a commodity, electricity is a quite distinct good for which there must be perfect adequacy of production and consumption at all time and characterized by a very inelastic demand. High shares of renewable energy sources lead to high price volatility and a higher risk to jeopardize the security of supply. Additionally, the switch to renewable energy sources will lead to an electrification of loads and transportation, and thus the emergence of new higher-consumption loads such as electric vehicles and heat pumps. These new and higher-consumption loads, combined with the population growth, will cause over-rated power load increases with less predictable load patterns in the future.This work focuses on issues specific to the distribution power grid in the context of the current energy transition. Traditional low-voltage grids are perhaps the most passive circuits in power grids. Indeed, they are designed primarily using a fit and forget approach where power flows go from the distribution transformer to the consumers and no element has to be operated or regularly managed. In fact, low-voltage networks completely lack observability due to very low monitoring. The distribution grid will especially undergo drastic changes from this energy transition. Distributed sources and new high-consumption -- and uncoordinated -- loads result in new power flow patterns, as well as exacerbated evening peaks for which it is not designed. The consequences are power overloads and voltage imbalances that deteriorate grid components, such as a main asset like the medium-to-low voltage transformer. Additionally, the distribution grid is characterized by end-users that pay a price for electricity that does not reflect the grid situation -- that is, mostly constant over a year -- and allow little to no actions on their consumption.These issues have motivated authorities to propose a global approach to ensure security of electricity supply at short and medium-term. The latter requires, among others, the development of demand response programs that encourage users to take advantage of load flexibility. First, we propose adequate electricity pricing structures that will allow users to unlock the potential of such demand response programs; namely, dynamic pricings combined with a prosumer structure. Second, we propose a fast and robust two-level optimization, formulated as a mixed-integer linear program, that coordinates flexible loads. We focus on two types of loads; electric vehicles and heat pumps, in an environment with solar PV panels. The lower level aims at minimizing individual electricity bills while, at the second level, we optimize the power load curve, either to maximize self-consumption, or to smoothen the total power load of the transformer. We propose a parametric study on the trade-off between only minimizing the individual bills versus only optimizing power load curves, which have proven to be antagonist objectives. Additionally, we assess the impact of the rising share of flexible loads and renewable energy sources for scenarios from today until 2050. A macro-analysis of the results allows us to assess the benefits of load flexibility for every actor of the distribution grid, and depending on the choice of a pricing structure. Our optimization has proved to prevent evening peaks, which increases the lifetime of the distribution transformer by up to 200%, while individual earnings up to 25% can be made using adequate pricings. Consequently, the optimization significantly increases the power demand elasticity and increases the overall welfare by 10%, allowing the high shares of renewable energy sources that are foreseen. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Cette thèse examine comment répondent des consommateurs aux mécanismes visant à réduire leur consommation d'énergie. Ce besoin de réduction découle de la nécessité d'atteindre les objectifs de réduction d'émissions de gaz à effet de serre, d'augmenter la production d'énergie à partir d'énergie renouvelables et de réaliser des économies d'énergie. Ces objectifs exigent que la demande résidentielle soit plus flexible face à l'évolution de l'offre et que des économies d'énergie soient réalisées par les ménages. Le premier chapitre explore les barrières à l'acceptation et à l'adoption des compteurs intelligents et des incitations qu'ils peuvent fournir. D'importantes barrières existent et les réductions de consommation sont loin d'être réalisées. Le manque de motivation, l'incompréhension de l'information sur la consommation et la rigidité de la vie quotidienne sont les principales barrières qui limitent la réponse des ménages aux incitations fournies par les compteurs intelligents. Le deuxième chapitre analyse les résultats d'expériences de terrain et d'études pilotes portant sur les impacts des différentes incitations sur la consommation résidentielle. Les résultats montrent qu'il existe de grandes variations et qu'en moyenne, une incitation entraînera une réduction de 2% de la consommation d'énergie. Les incitations de feedback en temps réel ainsi que l'information monétaire ont le plus grand effet. Enfin, les études plus robustes font état d'effets de réduction plus faibles. Dans le troisième chapitre, un jeu expérimental de ressources communes est utilisé pour explorer les réponses individuelles aux incitations basées sur le prix et les nudges. Les individus sont encouragés à réduire leur consommation, soit par une augmentation de prix, soit par des smiley évocant leur surconsommation. Le prix est le plus efficace pour encourager le niveau cible de consommation, mais il faut plus de temps pour qu'il fasse effet. Le nudge est compris rapidement mais tend à renforcer les comportements de surconsommation. Le quatrième chapitre examine l'effet du framing sur la disposition à l'effort. Les individus doivent accomplir une tâche simple et répétitive pour laquelle ils reçoivent un paiement à la pièce sous forme d'un gain ou d'une perte. Le framing sous forme de gains et de pertes est combiné à trois structures de paiement différentes : gain fixe, gain faible ou élevé avec une probabilité égale révélée avant ou après la réalisation de l'effort. Les résultats montrent que le framing n'a aucun effet sur la réalisation de l'effort, excepté pour un contexte de gain élevé annoncé avant de fournir l'effort. / This thesis examines how consumers respond to incentives used to encourage a reduction in their energy consumption. This necessary reduction stems from the need to reduce greenhouse gas emissions, increase energy production from renewable energy sources and achieve energy savings. These objectives require that residential demand be more flexible in response to changes in supply and that energy savings be achieved by households. The first chapter explores the barriers to consumer acceptance and adoption of smart meters and the incentives that they provide. Significant barriers exist and consumption reductions are far from being achieved. Limited motivation, lack of understanding of information on consumption and the rigidity of daily life are the main barriers preventing households from acting upon the incentives delivered via smart meters. The second chapter analyses the results of field experiments and pilot studies on the impacts of different incentives on residential consumption. The results show that there are large variations and that, on average, an incentive will result in a 2% reduction in energy consumption. Real-time feedback and monetary information have the greatest effect. Finally, more robust studies report lower reduction effects. In the third chapter, a common pool resource game is used to explore individual responses to price and nudge-based incentives. Individuals are encouraged to reduce their consumption either by price increases or by smilies that reflect their overconsumption. The price is most effective at encouraging the target level of consumption but takes longer to have an effect. The nudge is quickly understood but tends to reinforce overconsumption behaviours. The fourth chapter examines the effect of framing on effort provision. Individuals are asked to complete a simple and repetitive task for which they receive a piece-rate payoff in the form of a gain or loss. Framing in the form of gains and losses is combined with three different payment structures: fixed gain, low gain or high gain with an equal probability revealed before or after the effort is made. The results show that framing has no effect on effort provision, except for a high gain context announced before making the effort.

En 2010, ERDF (Electricité Réseau Distribution France) a entamé la mise en place du projet « Linky » dont l'objectif est d'installer 35 millions de compteurs intelligents en France. Ces compteurs permettront de collecter les données de consommation en « temps réel », avec lesquelles des modèles de charge plus précis pourront être envisagés. Dans ce contexte, cette thèse définit deux objectifs: la définition de modèles prédictifs de charge pour la conduite et la conception de modèles d'estimation de charge pour la planification. En ce qui concerne la conduite, nous avons développés deux modèles. Le premier exploite le formalisme mathématique des séries chronologiques ; le second est basé sur un réseau de neurones. Les deux modèles cherchent à prévoir la charge des jours « J+1 » et « J+2 » à partir des informations collectées jusqu'au jour « J ». Le modèle « série chronologique » repose sur les propriétés temporelles des courbes de charge. Ainsi on découpe la courbe de charge en trois parties : la tendance, la périodicité et le résidu. Les premiers deux sont déterministes et indépendamment développés en deux modèles : le modèle de tendance et le modèle de cyclicité. La somme de la prévision de ces deux modèles est la prévision finale. Le résidu quant à lui capture les phénomènes aléatoires que présente la courbe de charge. Le modèle de prédiction ainsi développé s'aide de nombreux outils statistiques (e.g., test de stationnarité, test ANOVA, analyse spectrale, entres autres) pour garantir son bon fonctionnement. Enfin, modèle « série chronologique » prend en compte plusieurs facteurs qui expliquent la variation dans la courbe de consommation tels que la température, les cyclicités, le temps, et le type du jour, etc. En ce qui concerne le modèle à base de réseaux de neurones, nous nous focalisons sur les stratégies de sélection de la structure pour un modèle optimal. Les choix des entrées et du nombre de neurones cachés sont effectués à travers les méthodes dites de «régression orthogonale » et de « leave-one-out-virtuel ». Les résultats montrent que la procédure proposée permet de choisir une structure de réseau de neurones qui garantisse une bonne précision de prédiction. En ce qui concerne la planification, un modèle non paramétrique est proposé et comparé avec le modèle actuel « BAGHEERA » d'EDF. Avec l'ouverture du marché d'électricité, la relation entre les fournisseurs, les clients et les distributeurs devient flexible. Les informations qualitatives d'un client particulier telles que sa puissance souscrite, son code d'activité, ses tarifs etc. sont de moins en moins disponibles. L'évolution du modèle BAGHEERA qui dépend ces informations pour classer les clients dans différentes catégories est devenue indispensable. Le modèle non paramétrique est un modèle individuel centré sur le relevé des compteurs. Trois variables de régression non paramétriques : Nadaraya Watson, Local Linear et Local Linear adapted ont été analysées et comparées. Les scénarios de validation montrent que le modèle non paramétrique est plus précis que le modèle « BAGHEERA ». Ces nouveaux modèles ont été conçus et validés sur de vraies données collectées sur le territoire français. / From 2010, ERDF (French DSO) started the “Linky” project. The project aims at installing 35 millions smart meters in France. These smart meters will collect individual client's consumption data in real time and transfer these data to the data center automatically in a certain frequency. These detailed consumption information provided by the smart metering system enables the designs of more accurate load models. On this purpose, two distinctive objectives are defined in this dissertation: the forecasting load models for the operation need and the estimation load models for the planning need. For the operation need, two models are developed, respectively relying on the “time series” and the “neural network” principals. They are both for the objective of predicting the loads in “D+1” and “D+2” days based on the historical information till “D” day. The “time series” model divides the load curve into three components: the trend, the cyclic, and the residual. The first two parts are deterministic, from which two models named the trend model and the cyclic model are made. The sum of the prevision of these two models is the final prediction result. For a better precision, numerous statistical tools are also integrated such that the stationary test, the smoothed periodogram, the ANOVA test and the gliding window estimation, etc. The time series model can extract information from the influence factors such as the time, the temperature, the periodicities and the day type, etc. Being the most popular non linear model and the universal approximator, the neural network load forecasting model is also studied in this dissertation. We focus on the strategy of the structure selection. The work is in collaboration with Prof. Dreyfus (SIGMA lab), a well known expert in the machine learning field. Input selection and model selection are performed by the “orthogonal forward regression” and the “virtual-leave-one-out” algorithms. Results show that the proposed procedure is efficient and guarantees the chosen model a good accuracy on the load forecasting. For the planning, a nonparametric model is designed and compared with the actual model “BAGHEERA” of the French electricity company EDF. With the opening of the electricity market, the relationship among the regulators, suppliers and clients is changing. The qualitative information about a particular client such as his subscribed power, his activity code and his electricity tariffs becomes less and less available. The evolution from the BAGHEERA model to a data-driven model is unavoidable, since the BAGHEERA model depends on these information to attribute every client in the French territory into a pre-defined category. The proposed nonparametric model is individualized and can deal with both temperature sensitive (possessing an electrical heater) and temperature insensitive clients. Three nonparametric regressors are proposed: the Nadaraya Watson, the local linear, and the local linear adapted. The validation studies show that the nonparametric model has a better estimation precision than the BAGHEERA model. These novel models are designed and validated by the real measurements collected in the French distribution network.

Orientador: Gustavo Fraidenraich / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T03:16:11Z (GMT). No. of bitstreams: 1 FloresRodriguez_AndreaCarolina_M.pdf: 1415054 bytes, checksum: 6b986968e8d7ec4e6459e4cea044d379 (MD5) Previous issue date: 2014 / Resumo: A compressão dos dados de consumo residencial de energia elétrica registrados torna-se extremadamente necessária em Smart Metering, a fim de resolver o problema de grandes volumes de dados gerados pelos medidores. A principal contribuição desta tese é a proposta de um esquema de representação teórica da informação registrada na forma mais compacta, sugerindo uma forma de atingir o limite fundamental de compressão estabelecido pela entropia da fonte sobre qualquer técnica de compressão disponibilizada no medidor. A proposta consiste na transformação de codificação dos dados, baseado no processamento por segmentação: no tempo em taxas de registros de 1/900 Hz a 1 Hz, e nos valores de consumo residencial de energia elétrica. Este último subdividido em uma compressão por amplitude mudando sua granularidade e compressão dos dados digitais para representar o consumo com o menor número de bits possíveis usando: PCM-Huffman, DPCM-Huffman e codificação de entropia supondo diferentes ordens de distribuição da fonte. O esquema é aplicado sobre dados modelados por cadeias de Markov não homogêneas para as atividades dos membros da casa que influenciam no consumo elétrico e dados reais disponibilizados publicamente. A avaliação do esquema é feita analisando o compromisso da compressão entre as altas taxas de registro, distorção resultante da digitalização dos dados, e exploração da correlação entre amostras consecutivas. Vários exemplos numéricos são apresentados ilustrando a eficiência dos limites de compressão. Os resultados revelam que os melhores esquemas de compressão de dados são encontrados explorando a correlação entre as amostras / Abstract: Data compression of recorded residential electricity consumption becomes extremely necessary on Smart Metering, in order to solve the problem of large volumes of data generated by meters. The main contribution of this thesis is to propose a scheme of theoretical representation of recorded information in the most compact form, which suggests a way to reach the fundamental limit of compression set by the entropy of the source, of any compression technique available in the meter. The proposal consists in the transformation of data encoding, based on the processing by segmentation: in time by registration rate from 1/900 Hz to 1 Hz, and in the values of residential electricity consumption. The latter is subdivided into compression: by amplitude changing their regularity, and digital data compression to represent consumption as few bits as possible. It is using PCM-Huffman, DPCM-Huffman and entropy encoding by assuming different orders of the source. The scheme is applied to modeled data by inhomogeneous Markov chains to create the activities of household members that influence electricity consumption, and real data publicly available. The assessment scheme is made by analyzing the trade off of compression between high registration rates, the distortion resulting from the digitization of data, and analyzing the correlation of consecutive samples. Several examples are presented to illustrate the efficiency of the compression limits. The analysis reveals that better data compression schemes can be found by exploring the correlation among the samples / Mestrado / Telecomunicações e Telemática / Mestra em Engenharia Elétrica