Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-10-24T12:50:06Z No. of bitstreams: 1 taisdemedeirospedreira.pdf: 1862768 bytes, checksum: 0c6ee31fd9be772b5b609051a207f61f (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-11-23T12:17:19Z (GMT) No. of bitstreams: 1 taisdemedeirospedreira.pdf: 1862768 bytes, checksum: 0c6ee31fd9be772b5b609051a207f61f (MD5) / Made available in DSpace on 2018-11-23T12:17:19Z (GMT). No. of bitstreams: 1 taisdemedeirospedreira.pdf: 1862768 bytes, checksum: 0c6ee31fd9be772b5b609051a207f61f (MD5) Previous issue date: 2018-09-05 / As previsões a curto prazo da carga elétrica (de algumas horas até alguns dias à frente) são essenciais para o planejamento, controle e operação dos sistemas de energia, tanto por por razões técnicas quanto financeiras. Como não é possível estocar grandes quantidades, torna-se indispensável um maneira eficaz de programar a produção da energia para que ela atenda a demanda. Por conta disso, uma grande literatura desenvolveu-se sobre o assunto. Devido à complexidade das séries de carga e à dependência não-linear destas carga em relação a diversas variáveis exógenas, os sistemas de previsão mais frequentemente propostos em trabalhos recentes são aqueles baseados em algoritmos complexos de inteligência computacional. No entanto, métodos lineares simples ainda são muito comumente usados, por si sós ou em combinação com técnicas não-lineares. Um desses métodos é o de Holt-Winters-Taylor, que é uma adaptação do conhecido método de amortecimento exponencial de Holt-Winters para que múltiplas sazonalidades possam ser modelados concomitantemente. Este trabalho implementa três variantes deste método HWT e analisa seus desempenhos em duas séries de dados reais de carga. Verificou-se que uma combinação linear dessas variantes nitidamente supera o método HWT original e fornece previsões precisas, com um baixo custo computacional. / Short-term load forecasts (forecasts for horizons ranging from a few hours to a few days ahead) are essential for the planning, controling and operation of energy systems, both for technical and financial reasons. Since it is not feasible to store energy in large quantities, an efficient way to forecast energy demand becomes indispensable. Because of this, a large literature has developed on the subject. Due to the complexity of load series and the nonlinear relationship of the load with exogenous variables, the most frequently proposed forecasting systems in recent papers are those based on complex algorithms of computational intelligence. However, simple linear methods are still very frequently used, either alone or in combination with non-linear techniques. One of these methods is Holt-Winters-Taylor (HWT), which is an adaptation of the well-known Holt-Winters exponential smoothing method, modified so that multiple seasonalities can be modeled at the same time. In this paper, we implement three variants of this HWT method and analyze their performances over two sets of actual load data. We found that a linear combination of these variants clearly outperforms the original HWT method, and provides accurate forecasts at a low computational cost.

[pt] Esta dissertação investiga o desempenho de técnicas de inteligência computacional na previsão de carga em curto prazo. O objetivo deste trabalho foi propor e avaliar sistemas de redes neurais, lógica nebulosa, neuro-fuzzy e híbridos para previsão de carga em curto prazo, utilizando como entradas variáveis que influenciam o comportamento da carga, tais como: temperatura, índice de conforto e perfil de consumo. Este trabalho envolve 4 etapas principais: um estudo sobre previsão de carga e sobre as variáveis que influenciam o comportamento da carga; um estudo da aplicação de técnicas de inteligência computacional em previsão de carga; a definição de sistemas de redes neurais, lógica fuzzy e neuro-fuzzy em previsão de carga; e estudo de casos. No estudo sobre previsão de carga, foi observada a influência de algumas variáveis no comportamento da curva de carga de uma empresa de energia elétrica. Entre estas variáveis se encontram alguns dados meteorológicos (Temperatura, Umidade, Luminosidade, Índice de conforto, etc.), além de informações sobre o perfil de consumo de carga das empresas. Também foi observado o comportamento da série de carga com relação ao dia da semana, sua sazonalidade e a correlação entre o valor atual e valores passados. Foi realizado um levantamento bibliográfico sobre a aplicação de técnicas de inteligência computacional na previsão de carga. Os modelos de redes neurais, são os mais explorados até o momento. Os modelos de lógica fuzzy começaram a ser utilizados mais recentemente. Modelos neuro-fuzzy são mais recentes que os demais, não existindo portanto, muita bibliografia a respeito. Os projetos de aplicação dos três modelos foram classificados quanto à sua arquitetura, desempenho, erros medidos, entradas utilizadas e horizonte da previsão. Foram propostos e implementados 4 sistemas de previsão de carga: lógica fuzzy, redes neurais, sistema neuro-fuzzy hierárquico e um sistema híbrido neural/neuro- fuzzy. Os sistemas foram especializados para cada dia da semana, pelo fato do comportamento da carga ser distinto entre estes dias. Para os sistemas neural, neuro-fuzzy e híbrido os dados também foram separados em inverno e verão, pois o perfil de consumo de carga é diferente nestas estações. O sistema com lógica fuzzy foi modelado para realizar previsões de curtíssimo prazo (10 em 10 minutos), utilizando para isto o histórico de carga, hora do dia e intervalo de dez minutos dentro da hora do dia. As regras do sistema foram geradas automaticamente a partir do histórico de carga e os conjuntos nebulosos foram pré-definidos. O sistema com redes neurais teve sua arquitetura definida através de experimentos, utilizando- se apenas dados de carga, hora do dia e mês como entradas. O modelo de rede escolhido foi com retropropagação do erro (backpropagation). Foram realizados testes incluindo outras entradas como temperatura e perfil de consumo. Para o sistema neuro-fuzzy foi escolhido um sistema neuro-fuzzy hierárquico, que define automaticamente sua estrutura e as regras a partir do histórico dos dados. Em uma última etapa, foi estudado um sistema híbrido neural/ neuro- fuzzy, no qual a previsão da rede neural é uma entrada do sistema neuro-fuzzy. Para os três últimos modelos as previsões realizadas foram em curto prazo, com um horizonte de uma hora Os sistemas propostos foram testados em estudos de casos e os resultados comparados entre si e com os resultados obtidos em outros projetos na área. Os dados de carga utilizados no sistema com lógica fuzzy foram da CEMIG, no período de 1994 a 1996, em intervalos de 10 minutos, para previsões em curtíssimo prazo. Os resultados obtidos podem ser considerados bons em comparação com um sistema de redes neurais utilizando os mesmos dados. Para os demais modelos foram utilizados os seguintes dados: dados horários de carga da Light e da CPFL, no períod / [en] This thesis examines the performance of computational intelligence in short term load forecasting. The main objective of the work was to propose and evaluate neural network, fuzzy logic, neurofuzzy and hybrid systems in the role of short term load forecast, considering some variables that affect the load behavior such as temperature, comfort indexes and consumption profile. The work consisted in four main steps: a study about load forecasting; the modeling of neural network systems, fuzzy logic and neurofuzzy related to load forecast; and case studies. In the load forecasting studies, some variables appeared to affect the behavior of the load curve in the case of electrical utilities. These variables include meteorological data like temperature, humidity, lightening, comfort indexes etc, and also information about the consumption profile of the utilities. It was also noted the distinct behavior of the load series related to the day of the week, the seasonableness and the correlation between the past and present values. A bibliographic research concerning the application of computational intelligence techniques in load forecasting was made. This research showed that neural network models have been largely employed. The fuzzy logic models have just started to be used recently. Neuro-fuzzy are very recent, and there are almost no references on it. The surveyed application projects using the three models were classified by its architecture, performance, measured errors, inputs considered and horizon of the forecast. In this work four systems were proposed and implemented for load forecasting: fuzzy logic, neural network, hierarchical neuro-fuzzy and hybrid neural/neuro- fuzzy. The systems were specialized for each day of the week, due to the different behavior of the load found for each of the days. For the neural network, neuro-fuzzy and hybrid, the data were separated in winter and summer, due to the different behavior of the load in each of the seasons. The fuzzy logic system was modeled for very short term forecasting using the historic load for each hour of the day, in steps of 10 minutes within each hour. The fuzzy system rules were generated automatically based on the historic load and the fuzzy sets were pre-defined. The system with neural network had its architecture defined through experiments using only load data, hour of the day and month as input. The network model chosen was the back- propagation. Tests were performed adding other inputs such as temperature and consumption profile. For the neural- fuzzy, a hierarchical neuro-fuzzy system, which defines automatically its structure and rules based on the historical data, was employed. In a further step, a hybrid neural/neuro-fuzzy was studied, so as the neural network forecast is the input for the neuro-fuzzy system. For the last three models, short term forecasting was made for one hour period. The proposed systems were tested in case studies, and the results were compared themselves and with results obtained in other projects in the same area. The load data of CEMIG between 1994 and 1996 was used in the fuzzy logic system in steps of 10 minutes for very short term forecasting. The performance was good compared with a neural network system using the same data. For the other models, short term load forecasting (I hour, 24 steps ahead) was done using the following data: load data of LIGHT and CPFL between 1996 and 1998; temperature (hourly for LIGHT and daily for CPFL); the codification of month and hour of the day; and a profile of load by consumption class. For doing. The error results obtained by the models were around 1,15% for the fuzzy logic, 2,0% for the neural network, 1,5% for the neuro-fuzzy system, and 2,0% for the hybrid system. This work has showed the applicability of the computational intelligence techniques on load forecasting, demonstrating that a preliminary study of the series and their relation with

[pt] Esta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na área de previsão de carga elétrica. Nesta investigação foram utilizados dados reais de energia relativos ao sistema elétrico brasileiro. O trabalho consiste de quatro partes principais: um estudo sobre o problema de previsão de carga no contexto de sistemas elétricos de potência; o estudo e a modelagem das RNAs para previsão de carga; o desenvolvimento do ambiente de simulação; e o estudo de casos. O estudo sobre o problema de previsão de carga envolveu uma investigação sobre a importância da previsão de demanda de energia na área de sistemas elétricos de potência. Enfatizou-se a classificação dos diversos tipos de previsão de acordo com o seu horizonte, curto e longo prazo, bem como a análise das variáveis mais relevantes para a modelagem da carga elétrica. O estudo também consistiu da análise de vários projetos na área de previsão de carga, apresentando as metodologias mais utilizadas. O estudo e a modelagem de RNAs na previsão de carga envolveu um extenso estudo bibliográfico de diversas metodologias. Foram estudadas as arquiteturas e os algoritmos de aprendizado mais empregados. Constatou-se uma predominância da utilização do algoritmo de retropropagação (Backpropagation) nas aplicações de previsão de carga elétrica horária para curto prazo. A partir desse estudo, e utilizando o algoritmo de retropropagação, foram propostas diversas arquiteturas de RNAs de acordo com o tipo de previsão desejada. O desenvolvimento do ambiente de simulação foi implementado em linguagem C em estações de trabalho SUN. O pacote computacional engloba basicamente 3 módulos: um módulo de pré-processamento da série de carga para preparar os dados de entrada; um módulo de treinamento da Rede Neural para o aprendizado do comportamento da série; e um módulo de execução da Rede Neural para a previsão dos valores futuros da série. A construção de uma interface amigável para a execução do sistema de previsão, bem como a obtenção de um sistema portátil foram as metas principais para o desenvolvimento do simulador. O estudo de casos consistiu de um conjunto de implementações com o objetivo de testar o desempenho de um sistema de previsão baseado em Redes Neurais para dois horizontes distintos: previsão horária e previsão mensal. No primeiro caso, foram utilizados dados de energia da CEMIG (Estado de Minas Gerais) e LIGHT (Estado do Rio de Janeiro). No segundo caso, foram utilizados dados de energia de 32 companhias do setor elétrico brasileiro. Destaca-se que a previsão mensal faz parte de um projeto de interesse da ELETROBRÁS, contratado pelo CEPEL. Para ambos os casos, investigou-se a influência do horizonte de previsão e da época do ano no desempenho do sistema de previsão. Além disso, foram estudadas as variações do desempenho das Redes Neurais de acordo com a empresa de energia elétrica utilizada. A avaliação do desempenho foi feita através da análise das seguintes estatísticas de erro: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Error) e U de Theil. O desempenho das RNAs foi comparado com o de outras técnicas de previsão, como os métodos de Holt-Winters e Box & Jenkins, obtendo-se resultados, em muitos casos, superiores. / [en] This dissertation investigates the application of Artificial Neural Networks (ANNs) in load forecasting. In this work we have used real load data from the Brazilian electrical system. The dissertation is divided in four main topics: a study of the importance of load forecasting to electric power systems; the investigation of the ANN modeling to this particular problem; the development of a neuro-simulador; and the case studies. It has been made an investigation of the objectives of load forecasting to power systems. The different kinds of load forecasting have been classified according to the leading time of the prediction (short and long term). The more important variables to model electric load were also investigated. This study analyses many projects in the area of load forecasting and presents the techniques that have been traditionally used to treat the problem. The ANNs modeling to load forecasting involved a deep investigation of works that have been published. The ANNs architectures and learning algorithms more commonly used were studied. It has been verified that the Backpropagation algorithm was the more commoly applied in the problem (particularly, in the problem of short term hourly load forecasting). Based on this investigation and using the backpropagation algorithm, many Neural Networks architetures were proposed according to the desired type of forecasting. The development of the neuro-simulator has been made in C language, using SUN workstations. The software is divided in 3 modules: a load series pre-processing module, to prepare the input data; a training module to the load series behavior learning; and an execution module, in which the Neural Network will perform the predictions. The development of a friendly interface to the forecasting system execution and the portability of the system were main goals during the simulator development. The case studies involved testing the system performance for 2 cases: hourly and monthly predictions. In the first case, load data from CEMING (State of Minas Gerais) and LIGHT (State of Rio de Janeiro) has been used. In the second case load data from 32 companies of the Brazilian electrical system has been used. Monthly load forecasting is involved in a project of interest of two companies of the electric sector in Brazil: CEPEL and ELETROBRÁS. In both cases, influences of the forecasting horizon and of the period of the year in the system´s performance has been investigated. Besides, the changes in the forecasting performance according to the particular electric company were also studied. The performance evaluation has been done through the analysis of the following error figures: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Error) and Theil´s U. The ANN performance was also compared with the performance of other techniques, like Holt-Winteres and Box-Jenkins, giving better results in many cases.

L'effacement des consommations électriques a été identifié comme l'une des solutions pour pallier les problèmes liés aux pics de consommation électrique, à l’intermittence des énergies renouvelables et à la congestion des réseaux. Ces travaux de recherche s’intéressent à l’intégration de la consommation industrielle en tant que ressources effaçables à court terme dans le contexte de la réserve rapide du mécanisme d’ajustement français. Parmi les différents secteurs, le secteur industriel présente un intérêt particulier en raison de l’importance de sa consommation. Afin d'intégrer ce type de consommation dans l’équilibre offre-demande, il est nécessaire de prévoir le comportement de ces consommations à court terme ainsi que d’évaluer la fiabilité de ces prévisions. Ainsi, différentes méthodes de prévision à très court-terme adaptées aux données et au contexte ont été déployées sur différents consommations disponibles à deux niveaux d’agrégation différents : site et usage industriel. Des indicateurs de performance adaptés aux contraintes opérationnelles, appelés "taux de fiabilité", sont proposés et calculés pour évaluer la performance des méthodes de prévision. Ce taux de fiabilité est estimé pour différentes heures de la journée pour les différents sites et usages industriels étudiés. Les taux de fiabilité estimés permettent d'évaluer le risque pour une consommation spécifique (au niveau du site ou au niveau de chaque usage industriel) de ne pas respecter des contraintes opérationnelles imposées à chaque instant de simulation. / Demand response has been identified as one of the solutions to overcome the problems associated with peaks in electricity consumption, intermittency of renewable energy and network congestion. This thesis focuses on the integration of industrial electricity consumptions as short-term demand response resources in the context of a supply-demand balancing mechanism in France. Among the various sectors, industrial electricity consumptions are of particular interest because of their orders of magnitude. In order to integrate these consumptions to the supply-demand balance, it is necessary forecast their behavior in the short term and to evaluate the reliability of these forecasts. Thus, different short-term load forecasting methods adapted to the data and to the operational context are implemented on different sets of industrial consumptions data at two different consumption levels: the industrial site and the end-point equipment consumption. Performance indicators adapted to operational constraints, called "trust factors" are proposed and calculated to evaluate the performance of the forecasting methods. These trust factors are estimated for different hours of the day for all the different studied industrial sites and workshops. The estimated trust are used to assess the risks for a specific consumption to not to respect the operational constraints at a moment a forecast is simulated. Demand response is considered to become one of the elements to be implemented in order to achieve a successful energy transition through a more flexible power system.

Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.

Maintaining the electricity balance between supply and demand is a challenge for electricity suppliers. If there is an under or overproduction, it entails financial costs and affects consumers and the climate. To better understand how to maintain the balance, can the suppliers use short-term forecasts of electricity load. Hence it is of paramount importance that the forecasts are reliable and of high accuracy. Studies show that time series modeling moves towards more data-driven methods, such as Artificial Neural Networks due to their ability to extract complex relationships and flexibility. This study evaluates the performance of a multivariate Deep Autoregressive Neural Network (DeepAR) in ashort-term forecasting scenario of electricity load, with forecasted weather parameters as exogenous covariates. This thesis’s goal is twofold: to test the performance in terms of evaluation metrics of day-ahead forecasts in exogenous covariates’ presence and examine the robustness when exposing DeepAR to deviations in input data. We perform feature selection on given covariates to identify and extract relevant parameters to facilitate the training process and implement a feature importance algorithm to examine which parameters the model considers essential. To test the robustness, we simulate two cases. In the first case, we introduce Quarantine periods, which mask data prior to the forecast range, and the second case introduces an artificial outlier. An exploratory analysis displays significant annual characteristic differences between seasons, therefore do we use two test sets, one in winter and one in summer. The result shows that DeepAR is robust against potential deviations in input data and that DeepAR surpassed both benchmark models in all of the tested scenarios. In the ideal test scenario where weather parametershad the most significant impact (winter), do DeepAR achieve a Normalized Deviation(ND) of 2.5%, compared to the second-best model, with an ND of 4.4%

No description available.

This thesis examines the concept of time series forecasting. More specifically, it predicts the load and power demand in Sulaymaniyah, Iraqi Kurdistan, who are today experiencing frequent power shortages. This study applies a commonly used time series model, the autoregressive integrated moving average model, which is compared to the naïve method. Several key model properties are inspected to evaluate model accuracy. The model is then used to forecast the load and the demand on a daily, weekly and monthly basis. The forecasts are evaluated by examining the residual metrics. Furthermore, the quantitative results and the answers collected from interviews are used as a basis to investigate the conditions of capacity planning in order to determine a suitable strategy to minimize the unserved power demand. The findings indicate an unsustainable over consumption of power in the region due to low tariffs and subsidized energy. A suggested solution is to manage power demand by implementing better strategies such as increasing tariffs and to use demand forecast to supply power accordingly. The monthly supply forecast in this study outperforms the baseline method but not the demand forecast. On weekly basis, both the load and the demand models underperform. The performance of the daily forecasts performs equally or worse than the baseline. Overall, the supply predictions are more precise than the demand predictions. However, there is room for improvement regarding the forecasts. For instance, better model selection and data preparation can result in more accurate forecasts. / Denna studie undersöker prediktion av tidserier. Den tittar närmare på last- och effektbehov i Sulaymaniyah i Irak som idag drabbas av regelbunden effektbrist. Rapporten applicerar en vedertagen tidseriemodell, den autoregressiva integrerade glidande medelvärdesmodellen, som sedan jämförs med den naiva metoden. Några karaktäristiska modellegenskaper undersöks för att evaluera modellens noggrannhet. Den anpassade modellen används sedan för att predikera last- och effektbehovet på dags-, månads-, och årsbasis. Prognoserna evalueras genom att undersöka dess residualer. Vidare så användas de kvalitativa svaren från intervjuerna som underlag för att undersöka förutsättningarna för kapacitetsplanering och den strategi som är bäst lämpad för att möta effektbristen. Studien visar att det råder en ohållbar överkonsumtion av energi i regionen som konsekvens av låga elavgifter och subventionerad energi. En föreslagen lösning är att hantera efterfrågan genom att implementera strategier som att höja elavgifter men även försöka matcha produktionen med efterfrågan med hjälp av prognoser. De månadsvisa prognoserna för produktionen i studien överträffar den naiva metoden men inte för prognoserna för efterfrågan. På veckobasis underpresterar båda modellerna. De dagliga prognoserna presterar lika bra eller värre än den naiva metoden. I sin helhet lyckas modellerna förutspå utbudet bättre än efterfrågan på effekt. Men det finns utrymme för förbättringar. Det går nog att uppnå bättre resultat genom bättre förbehandling av data och noggrannare valda tidseriemodeller.

The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated. / Doctor of Philosophy / The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.

It is more than a fact that electrical energy is a main production factor of every economic activity. Since electrical power is not easy to store, it needs to be consumed as it is generated in order to keep a constant balance between supply and demand. As a result, for developing an efficient energy market it is significant to create a method for accurately forecasting the electricity consumption. This thesis describes a method for analyzing data provided by the ENTSO-E transparency platform. The ENTSO-E (European Network of Transmission System Operators) is a network of electricity operators from 36 countries across Europe. Its main objective is to provide transparency concerning data of electricity generation and consumption in Europe in order to promote the development of efficient and competitive electricity markets. By using the method described in this thesis, one may use historical data provided by ENTSO-E to forecast the electricity consumption of an EU country for the years to come. As an example, data of electricity consumption in Greece during the years 2015-2018 have been used in order to calculate the average load demand of a weekday during the year 2030. On the other hand, in order to correctly predict the electricity demand of a specific region over the next decade, one should take into account some crucial parameters that may influence not only the evolution of the load demand, but also the fuel mix that will be used in order to cover our future electricity needs. Advances in power generation technologies, evolution of fuel prices, expansion of electricity grid and economic growth are a subset of parameters that should be taken into account for an accurate forecast of the electricity consumption in the long run. Particularly for Greece, a set of parameters that may affect the electricity consumption are being computationally analyzed in order to evaluate their contribution to the load demand curve by the year 2030. These include the interconnection of Greek islands to the mainland, the development of Hellinikon Project and the increase of the share of electric vehicles. The author of this thesis has developed code in Python programming language that can be found in the Appendix. These scripts and functions that implement most of the calculations described in the following chapters can also be used for forecasting the load demand of other EU countries that are included in the ENTSO-E catalogue. The datasets used as input to these algorithms may also be used from the readers to identify more patterns for predicting the load demand for a specific region and time. A sustainable energy system is based on consumers with environmental awareness. As a result, citizens living inside the European Union should become a member of a community that promotes energy saving measures, investments in renewable energy sources and smart metering applications.