Spatio-Temporal Wind Speed Prediction Based on Improved Residual Shrinkage Network

Xinhao Liang, Feihu Hu (), Xin Li, Lin Zhang, Hui Cao and Haiming Li
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Xinhao Liang: State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiao Tong University, Xi’an 710049, China
Feihu Hu: State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiao Tong University, Xi’an 710049, China
Xin Li: State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiao Tong University, Xi’an 710049, China
Lin Zhang: State Grid Shaanxi Electric Power Company, Xi’an 710000, China
Hui Cao: State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiao Tong University, Xi’an 710049, China
Haiming Li: China Southern Power Grid Guangxi Power Grid Co., Ltd., Nanning 530000, China

Sustainability, 2023, vol. 15, issue 7, 1-19

Abstract: Considering the massive influx of new energy into the power system, accurate wind speed prediction is of great importance to its stability. Due to the influence of sensor accuracy and harsh natural environments, there is inevitable noise interference in original wind speed data, which adversely affects the accuracy of wind speed prediction. There are some problems associated with traditional signal processing methods when dealing with noise such as signal loss. We propose the use of a deep residual shrinkage unit based on soft activation (SDRSU) in order to reduce noise interference and ensure the integrity of original wind speed data. A deep network is constructed by stacking multiple SDRSUs to extract useful features from noisy data. Considering the spatio-temporal coupling relationship between wind turbines in a wind farm, a ST-SDRSN (soft-activation based deep spatio-temporal residual shrinkage network) will be used to model the wind speed series neighboring time property and daily periodic property. An accurate wind speed prediction can be achieved by extracting the spatial correlations between the turbines at each turbine along the time axis. We designed four depth models under the same spatio-temporal architecture to verify the advantages of the soft-activation block and the proposed ST-SDRSN model. Two datasets provided by the National Renewable Energy Laboratory (NREL) were used for our experiments. Based on different kinds of evaluation criteria in different datasets, ST-SDRSN was shown to improve prediction accuracy by 15.87%.

Keywords: wind speed prediction; noise processing; deep spatio-temporal model; ST-SDRSN; residual shrinkage network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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