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Multi-step Forecasting Model of Wind Speed Considering Influence of Typhoon
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Affiliation:

College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Abstract:

The reasonable cognition of the influence of typhoons on regional wind speed forecasting is crucial to the maximum utilization of wind power in the future. Based on the multi-model ensembled numerical weather forecasting information of typhoon, a multi-step wind speed forecasting model considering the influence of typhoons is proposed. In view of the noise of wind speed data during typhoons, empirical wavelet transform (EWT) is used to deconstruct the historical data of wind speed and the noise disturbance is eliminated based on the adaptive threshold method. The sequence signal of wind speed is reconstructed. Then the multi-step prediction of the reconstructed wind speed series is carried out by using gated recurrent unit (GRU) network to obtain the prediction information of wind speed without consideration of the influence of typhoons. For the lack of data during typhoons, deep belief network (DBN) is introduced to realize correction under the condition of typhoon and improve the accuracy of wind speed forecasting considering the influence of typhoons. Finally, a case study is carried out based on the actual data of a weather station in southern China and compared with the fundamental case without consideration of numerical forecasting information of typhoons. The result shows that compared with the baseline model without considering the influence of typhoons, the proposed model can effectively reduce the forecasting errors of wind speed.

Keywords:

Foundation:

This work is supported by the Fundamental Research Funds for the Central Universities (No. YJ201654).

Get Citation
[1]WEI Xiangyu, XIANG Yue, SHEN Xiaodong, et al. Multi-step Forecasting Model of Wind Speed Considering Influence of Typhoon[J]. Automation of Electric Power Systems,2021,45(14):30-37. DOI:10.7500/AEPS20201005003
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History
  • Received:October 05,2020
  • Revised:December 26,2020
  • Adopted:
  • Online: July 21,2021
  • Published: