ZHANG J P, YU X J, CHEN D, et al. An improved RCSA-ANN model for the prediction of offshore short-term wind speed[J]. Acta Aerodynamica Sinica, 2022, 40(4): 110−116. DOI: 10.7638/kqdlxxb-2020.0172
Citation: ZHANG J P, YU X J, CHEN D, et al. An improved RCSA-ANN model for the prediction of offshore short-term wind speed[J]. Acta Aerodynamica Sinica, 2022, 40(4): 110−116. DOI: 10.7638/kqdlxxb-2020.0172

An improved RCSA-ANN model for the prediction of offshore short-term wind speed

More Information
  • Received Date: December 10, 2020
  • Revised Date: August 30, 2021
  • Accepted Date: September 10, 2021
  • Available Online: December 26, 2021
  • In order to improve the prediction accuracy of offshore short-term wind speed, a model based on the random cuckoo search algorithm (RCSA) and artificial neural network (ANN) was proposed. Firstly, RCSA was obtained by introducing a random factor into CSA, and then a RCSA-ANN model for predicting offshore short-term wind speed was established. Secondly, the training of the model was carried out by using the offshore meteorological data measured at the wind tower in Luchao Port, Shanghai. Finally, the precision of RCSA-ANN model was verified by compariative analyses. Results show that the improved CSA method is simple, reliable, and effective. And it is not easy to fall into local optimum like other models. Moreover, the average error of the RCSA-ANN model is lower than those of the BP-ANN and CSA-ANN models. Since the RCSA-ANN model can predict fluctuating wind speed sequences with high prediction accuracy, it has a promising potential in the meteorological field.
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