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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Previsão do consumo de energia elétrica a curto prazo, usando combinações de métodos univariados

Carneiro, Anna Cláudia Mancini da Silva 26 September 2014 (has links)
Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-02T12:24:39Z No. of bitstreams: 1 annaclaudiamancinidasilvacarneiro.pdf: 1333903 bytes, checksum: a7b3819bb5b0e1adb8efd07bca0f9aa2 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-06T19:35:55Z (GMT) No. of bitstreams: 1 annaclaudiamancinidasilvacarneiro.pdf: 1333903 bytes, checksum: a7b3819bb5b0e1adb8efd07bca0f9aa2 (MD5) / Made available in DSpace on 2017-03-06T19:35:55Z (GMT). No. of bitstreams: 1 annaclaudiamancinidasilvacarneiro.pdf: 1333903 bytes, checksum: a7b3819bb5b0e1adb8efd07bca0f9aa2 (MD5) Previous issue date: 2014-09-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A previsão de cargas elétricas é fundamental para o planejamento das empresas de energia. O foco deste estudo são as previsões a curto prazo; assim, aplicamos métodos univariados de previsão de séries temporais a uma série real de cargas elétricas de 104 semanas no Rio de Janeiro, nos anos de 1996 e 1997, e experimentamos várias combinações dos métodos de melhor desempenho. As combinações foram feitas pelo método outperformance, uma combinação linear simples, com pesos fixos. Os resultados das combinações foram comparados ao de simulações de redes neurais artificiais que solucionam o mesmo problema, e ao resultado de um método de amortecimento de dupla sazonalidade aditiva. No geral, este método de amortecimento obteve os melhores resultados, e talvez seja o mais adequado e confiável para aplicações práticas, embora necessite de melhorias para garantir a extração completa da informação contida nos dados. / Forecasting the demand for electric power is crucial for the production planning in energy utilities. The focus of this study are the short-term forecasts. We apply univariate time series methods to the forecasting of a series containing observations of the energy consumption of 104 weeks in Rio de Janeiro, in 1996 and 1997, and experiment with several combinations of the methods which have the best performance. These combinations are done by the outperformance method, a simple linear combination with fixed weights. The results were compared to those obtained by neural networks on the same problem, and with the results of a exponential smoothing method for dual additive seasonality. Overall, the exponential smoothing method achieved the best results, and was shown to be perhaps the most reliable and suitable for practical applications, even though it needs improvements to ensure complete extraction of the information contained in the data.

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