Modelo híbrido baseado em séries temporais e redes neurais para previsão da geração de energia eólica

2018-11-202018-11-202018-08-30ALENCAR, David Barbosa de. Modelo híbrido baseado em séries temporais e redes neurais para previsão da geração de energia eólica. 2018. 119 f. Orientadora: Carolina de Mattos Affonso; Coorientador: Roberto Célio Limão de Oliveira. Tese (Doutorado em Engenharia Elétrica) - Instituto de Tecnologia, Universidade Federal do Pará, Belém, 2018.Disponível em: <http://repositorio.ufpa.br/jspui/handle/2011/10416>. Acesso em:.https://repositorio.ufpa.br/handle/2011/10416The electric power generation through wind turbines is one of the practically inexhaustible alternatives sources of electric power. It is considered a source of clean energy, but still requires a lot of research to develop science and technologies that ensure uniformity in generation, providing a greater participation of this source in the energy matrix in Brazil as in the world, because the wind presents abrupt variations speed, density, and other important variables. In wind-based electrical systems, each forecast horizon is applied to a specific segment, forecast of minutes, hours, weeks, months, and future years of wind behavior, in order to evaluate the availability of energy for the next period, relevant information in the dispatch of the generating units and in the control of the electric system. This thesis aimed to develop ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using Artificial Neural Networks, SARIMA models and hybrid models and to predict the generation capacity of power for each horizon. For the application of the methodology, the meteorological variables of the database of the national environmental data system SONDA, Petrolina station, were used for the period from January 1st, 2004 to March 31st, 2017. The performance of the models was compared with 5, 10 and 20 steps forward, considering minutes, hours, days, weeks, months and years as the forecast horizon. The hybrid model obtained better response in the forecasts, among which the hour horizon was highlighted.Acesso AbertoEnergia eólicaVelocidade do ventoSéries temporaisSarima (Média móvel integrada autoregressiva sazonal)Redes neurais artificiaisWind powerWind speedTime seriesSARIMA ( Seasonal autoregressive integrated moving average)Artificial neural networksModelo híbrido baseado em séries temporais e redes neurais para previsão da geração de energia eólicaHybrid model based on time series and neural networks to predict the generation of wind energyTeseCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::SISTEMAS ELETRICOS DE POTENCIASISTEMAS ELÉTRICOS DE POTÊNCIASISTEMAS DE ENERGIA ELÉTRICA