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Energies 2017, 10(12), 1976;

Different Models for Forecasting Wind Power Generation: Case Study

Department of Electrical Engineering, Federal University of Para—UFPA, Belém 66075-110, Brazil
Department of Industrial Engineering, Universidade Federal da Bahia, Salvador 40170-115, Brazil
Department of Research, Institute of Technology and Education Galileo of Amazon—ITEGAM, Manaus 69020-030, Brazil
Department of Postgraduate Curses, IDAAM., Manaus 69055-038, Brazil
Author to whom correspondence should be addressed.
Received: 23 October 2017 / Revised: 20 November 2017 / Accepted: 23 November 2017 / Published: 29 November 2017
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Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA) and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA), Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator. View Full-Text
Keywords: wind power; wind speed; time series; ARIMA; forecasting; wavelets wind power; wind speed; time series; ARIMA; forecasting; wavelets

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Barbosa de Alencar, D.; de Mattos Affonso, C.; Limão de Oliveira, R.C.; Moya Rodríguez, J.L.; Leite, J.C.; Reston Filho, J.C. Different Models for Forecasting Wind Power Generation: Case Study. Energies 2017, 10, 1976.

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