Hybrid model of time series forecasting for possible applications in the wind power sector





Statistical model, Artificial neural networks, Wind speed


In this paper an innovative hybrid model of time series prediction based on the combination of two functions (linear and nonlinear) of the Holt-Winters and Artificial Neural Networks models is presented. This model is applied in wind speed in northeastern Brazil, and was able to perform short and long term forecasts with good accuracy. We highlight the efficiency of the proposed model in providing perfect adjustments to the data observed, being this affirmative according to the low values found in the statistical analysis of errors, for example, with percentage error of approximately 5.0%, and also with the value of the Nash-Sutcliffe coefficient of efficiency of approximately 0.96. These results were important for the accuracy of the data, so that they could follow the profile of the observed time series, mainly revealing greater similarities of maximum and minimum values between both series, thus showing the capacity of the model to represent characteristics of local seasonality. Wind speed prediction methods can be a useful technique in the wind power sector, for example, being able to acquire important information on how local wind potential can be harnessed for possible electric power generation.


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Author Biographies

João Bosco Verçosa Leal Junior, Universidade Estadual do Ceará

Professor Associado, Curso de Física, Universidade Estadual do Ceará, UECE

Henrique do Nascimento Camelo, Universidade Federal do Rio Grande do Norte

Programa de Pós-Graduação em Ciências Climáticas

Paulo Sérgio Lucio, Universidade Federal do Rio Grande do Norte

Programa de Pós-Graduação em Ciências Climáticas

Paulo César Marques de Carvalho, Universidade Federal do Ceará

Departamento de Engenharia Elétrica


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How to Cite

Leal Junior, J. B. V., Camelo, H. do N., Lucio, P. S., & Carvalho, P. C. M. de. (2018). Hybrid model of time series forecasting for possible applications in the wind power sector. Ciência E Natura, 40, 01–06. https://doi.org/10.5902/2179460X30415

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