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Abstract(s)
A presente dissertação apresenta o estudo de previsão do diagrama de carga de subestações da Rede Elétrica Nacional (REN) utilizando redes neuronais, com o intuito de verificar a viabilidade do método utilizado, em estudos futuros.
Atualmente, a energia elétrica é um bem essencial e desempenha um papel fundamental, tanto a nível económico do país, como a nível de conforto e satisfação individual. Com o desenvolvimento do setor elétrico e o aumento dos produtores torna-se importante a realização da previsão de diagramas de carga, contribuindo para a eficiência das empresas.
Esta dissertação tem como objetivo a utilização do modelo das redes neuronais artificiais (RNA) para criar uma rede capaz de realizar a previsão de diagramas de carga, com a finalidade de oferecer a possibilidade de redução de custos e gastos, e a melhoria de qualidade e fiabilidade. Ao longo do trabalho são utilizados dados da carga (em MW), obtidos da REN, da subestação da Prelada e dados como a temperatura, humidade, vento e luminosidade, entre outros. Os dados foram devidamente tratados com a ajuda do software Excel. Com o software MATLAB são realizados treinos com redes neuronais, através da ferramenta Neural Network Fitting Tool, com o objetivo de obter uma rede que forneça os melhores resultados e posteriormente utiliza-la na previsão de novos dados.
No processo de previsão, utilizando dados reais das subestações da Prelada e Ermesinde referentes a Março de 2015, comprova-se que com a utilização de RNA é possível obter dados de previsão credíveis, apesar de não ser uma previsão exata. Deste modo, no que diz respeito à previsão de diagramas de carga, as RNA são um bom método a utilizar, uma vez que fornecem, à parte interessada, uma boa previsão do consumo e comportamento das cargas elétricas.
Com a finalização deste estudo os resultados obtidos são no mínimo satisfatórios. Consegue-se alcançar através das RNA resultados próximos aos valores que eram esperados, embora não exatamente iguais devido à existência de uma margem de erro na aprendizagem da rede neuronal.
This dissertation presents a study about forecasting electrical load diagram from substations in the national grid, specifically Rede Eléctrica Nacional (REN), using neural networks in order to verify the feasibility of this method in future studies. Currently, electricity is an essential and plays a key role, both in the country economy, as for the comfort and well-being of individuals. With development of the electricity sector and the increase of producers becomes important to perform predictions of the load diagrams, contributing to the efficiency of these companies. This dissertation aims to use the model of artificial neural networks (ANN) to create a network capable of predicting electrical load diagrams, in order to offer the possibility of reducing costs and expenses, and improve quality and reliability. Throughout this project are used load data (in MW), gathered by REN, from the substation in Prelada and data such as temperature, humidity, wind and luminosity, among others. All this data is treated using the software Excel. With the software MATLAB are performed trainings sessions with neural networks, using the tool Neural Network Fitting Tool, in order to obtain the network that provides the best results and thereafter use it to predict new data. In the prediction process, using real data from substations in Prelada and Ermesinde for the month of March 2015, is verified that with the use of ANN it is possible to obtain reliable and satisfactory prediction data, although not an accurate prediction. Thus, with regard to the load diagram prediction, the ANN are a good method to use since they provide, to interested party, a good prediction of the consumption and behavior of electric loads. With the completion of this study the results are, at least, satisfactory. Results close to the values that were expected can be achieved. Taking into account the fact that the values are not exactly like the original values, due to the existence of a margin of error in the neural network learning process.
This dissertation presents a study about forecasting electrical load diagram from substations in the national grid, specifically Rede Eléctrica Nacional (REN), using neural networks in order to verify the feasibility of this method in future studies. Currently, electricity is an essential and plays a key role, both in the country economy, as for the comfort and well-being of individuals. With development of the electricity sector and the increase of producers becomes important to perform predictions of the load diagrams, contributing to the efficiency of these companies. This dissertation aims to use the model of artificial neural networks (ANN) to create a network capable of predicting electrical load diagrams, in order to offer the possibility of reducing costs and expenses, and improve quality and reliability. Throughout this project are used load data (in MW), gathered by REN, from the substation in Prelada and data such as temperature, humidity, wind and luminosity, among others. All this data is treated using the software Excel. With the software MATLAB are performed trainings sessions with neural networks, using the tool Neural Network Fitting Tool, in order to obtain the network that provides the best results and thereafter use it to predict new data. In the prediction process, using real data from substations in Prelada and Ermesinde for the month of March 2015, is verified that with the use of ANN it is possible to obtain reliable and satisfactory prediction data, although not an accurate prediction. Thus, with regard to the load diagram prediction, the ANN are a good method to use since they provide, to interested party, a good prediction of the consumption and behavior of electric loads. With the completion of this study the results are, at least, satisfactory. Results close to the values that were expected can be achieved. Taking into account the fact that the values are not exactly like the original values, due to the existence of a margin of error in the neural network learning process.
Description
Keywords
Redes neuronais artificiais Rede elétrica Previsão de diagrama de carga Artificial neural networks Electric grid Forecast of load diagrams