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Different Models for Forecasting Wind Power Generation: Case Study

Author

Listed:
  • David Barbosa de Alencar

    (Department of Electrical Engineering, Federal University of Para—UFPA, Belém 66075-110, Brazil)

  • Carolina De Mattos Affonso

    (Department of Electrical Engineering, Federal University of Para—UFPA, Belém 66075-110, Brazil)

  • Roberto Célio Limão de Oliveira

    (Department of Electrical Engineering, Federal University of Para—UFPA, Belém 66075-110, Brazil)

  • Jorge Laureano Moya Rodríguez

    (Department of Industrial Engineering, Universidade Federal da Bahia, Salvador 40170-115, Brazil)

  • Jandecy Cabral Leite

    (Department of Research, Institute of Technology and Education Galileo of Amazon—ITEGAM, Manaus 69020-030, Brazil)

  • José Carlos Reston Filho

    (Department of Postgraduate Curses, IDAAM., Manaus 69055-038, Brazil)

Abstract

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.

Suggested Citation

  • David Barbosa de Alencar & Carolina De Mattos Affonso & Roberto Célio Limão de Oliveira & Jorge Laureano Moya Rodríguez & Jandecy Cabral Leite & José Carlos Reston Filho, 2017. "Different Models for Forecasting Wind Power Generation: Case Study," Energies, MDPI, vol. 10(12), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1976-:d:120857
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    References listed on IDEAS

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    14. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
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