Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit

Yang, Kui1; Wang, Bofu2,3; Qiu, Xiang1; Li, Jiahua4; Wang, Yuze5; Liu, Yulu1,2

2022-06-01

10.3390/en15124221

ENERGIES   影响因子和分区

1996-1073

Accurate wind speed prediction is a premise that guarantees the reliable operation of the power grid. This study presents a combined prediction model that integrates data preprocessing, cascade optimization, and deep learning prediction to improve prediction performance. In data preprocessing, the wavelet soft threshold denoising (WSTD) is employed to filter the blurring noise of the original data. Then, the robust empirical mode decomposition (REMD) and adaptive variational mode decomposition (AVMD) are adopted to carry out a two-stage adaptive decomposition. Spearman correlation is used to quantify the mode that need to be decomposed for the second time. In the cascade optimization, the hybrid grey wolf algorithm (HGWO) is employed to optimize the parameters of the VMD and the gated recurrent unit (GRU), which overcomes the problem of empirical parameter adjustment. The HGWO is also adopted in the prediction strategy to optimize the GRU model to predict the grouped intrinsic mode functions (IMFs). Lastly, the final wind speed prediction result is obtained by superimposing the values of all the predicted models. The proposed model was validated with the measured wind speed data of the four quarters in the Bay area of China and was compared with 20 models of the classic method to further evaluate the effectiveness of the model. The results show that the whole process of the proposed model is adaptive, the final multi-step prediction performance is good, and high prediction accuracy can be attained.

wind speed prediction wavelet soft threshold denoising robust empirical mode decomposition cascade optimization strategy deep gated recurrent unit adaptive model

SCI ; EI

英语

通讯

National Natural Science Foundation of China[91952102,12032016,11972220] ; Shanghai Municipal Education Commission in China[18SG53] ; Guangdong Provincial Key Laboratory[2019B121203001]

Energy & Fuels

Energy & Fuels

WOS:000815908300001

MDPI

20222512254049

Deep learning ; Electric power transmission networks ; Forecasting ; Intrinsic mode functions ; Speed ; Wavelet decomposition ; Wind speed

Ergonomics and Human Factors Engineering:461.4 ; Wind Power (Before 1993, use code 611 ):615.8 ; Electric Power Transmission:706.1.1 ; Information Theory and Signal Processing:716.1 ; Mathematical Transformations:921.3

Web of Science

1.Shanghai Inst Technol, Sch Sci, Shanghai 201418, Peoples R China
2.Shanghai Univ, Shanghai Frontiers Sci Base Mechanoinfomat, Shanghai Inst Appl Math & Mech, Sch Mech & Engn Sci,Shanghai Key Lab Mech Energy, Shanghai 200072, Peoples R China
3.Southern Univ Sci & Technol, Guangdong Prov Key Lab Turbulence Res & Applicat, Shenzhen 518055, Peoples R China
4.Shanghai Inst Technol, Sch Urban Construct & Safety Engn, Shanghai 201418, Peoples R China
5.Shanghai Inst Technol, Sch Mech Engn, Shanghai 201418, Peoples R China

Yang, Kui,Wang, Bofu,Qiu, Xiang,et al. Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit[J]. ENERGIES,2022,15(12).

Yang, Kui,Wang, Bofu,Qiu, Xiang,Li, Jiahua,Wang, Yuze,&Liu, Yulu.(2022).Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit.ENERGIES,15(12).

Yang, Kui,et al."Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit".ENERGIES 15.12(2022).