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Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system

Khosravi, A. ; Koury, R.N.N. ; Machado, L. ; Pabon, J.J.G.

Sustainable energy technologies and assessments, 2018-02, Vol.25, p.146-160

Elsevier Ltd

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  • Título:
    Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system
  • Autor: Khosravi, A. ; Koury, R.N.N. ; Machado, L. ; Pabon, J.J.G.
  • Assuntos: ANFIS ; Partial swarm optimization ; Support vector regression ; Wind direction ; Wind energy
  • É parte de: Sustainable energy technologies and assessments, 2018-02, Vol.25, p.146-160
  • Descrição: •Using MLAs to predict wind speed, wind direction and output power of a wind turbine.•MLFFNN, SVR and ANFIS methods are the forecasting models.•Partial swarm optimization algorithm is used to optimize the ANFIS model.•Energy and exergy analysis was done for the considered wind turbine (E-44, 900kW). In this study, three models of machine learning algorithms are implemented to predict wind speed, wind direction and output power of a wind turbine. The first model is multilayer feed-forward neural network (MLFFNN) that is trained with different data training algorithms. The second model is support vector regression with a radial basis function (SVR-RBF). The third model is adaptive neuro-fuzzy inference system (ANFIS) that is optimized with a partial swarm optimization algorithm (ANFIS-PSO). Temperature, pressure, relative humidity and local time are considered as input variables of the models. A large set of wind speed and wind direction data measured at 5-min, 10-min, 30-min and 1-h intervals are utilized to accurately predict wind speed and its direction for Bushehr. Energy and exergy analysis of wind energy for a wind turbine (E-44, 900 kW) is done. Also, the developed models are used to predict the output power of the wind turbine. Comparison of the statistical indices for the predicted and actual data indicate that the SVR-RBF model outperforms the MLFFNN and ANFIS-PSO models. Also, the current energy and exergy analysis presents an average of 32% energy efficiency and approximately 25% exergy efficiency of the wind turbine in the study region.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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