SHORT-TERM WIND POWER PREDICTION BASED ON SVD AND KALMAN FILTER CORRECTION OF MULTI-POSITION NWP

Wang Lijie; Liu Tianmeng; Wang Bo; Hao Ying; Wang Zheng; Zhang Yuanpeng

doi:10.19912/j.0254-0096.tynxb.2021-0597

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 392-398. DOI: 10.19912/j.0254-0096.tynxb.2021-0597

SHORT-TERM WIND POWER PREDICTION BASED ON SVD AND KALMAN FILTER CORRECTION OF MULTI-POSITION NWP

Wang Lijie¹, Liu Tianmeng², Wang Bo³, Hao Ying¹, Wang Zheng³, Zhang Yuanpeng⁴

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Abstract

In consideration of the influences of positional and systematic errors in numerical weather prediction （NWP） grid point on the short-term wind power prediction accuracy, this paper puts forward a short-term wind power prediction model for the correction of multi-position NWP based on the singular value decomposition （SVD） and Kalman filtering, which firstly conducts the feature extraction as well as dimension-reduction process on the multi-position NWP, uses Kalman filtering method to correct the data of wind speed in NWP and to reduce the systematic error of NWP, and finally uses the corrected NWP data to build the short-term wind power prediction model based on the extreme random forest algorithm. Through the simulation of one wind farm as well as the comparison with single-position, non-dimension-reduction and uncorrected models, the results indicate that the dimension-reduction and corrected models have the best prediction effects, and the average error and root-mean-square error （RMSE） are 7.94% and 9.96%, respectively.

Key words

wind power prediction / numerical weather prediction / singular value decomposition / Kalman filter / extreme random forest

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Wang Lijie, Liu Tianmeng, Wang Bo, Hao Ying, Wang Zheng, Zhang Yuanpeng. SHORT-TERM WIND POWER PREDICTION BASED ON SVD AND KALMAN FILTER CORRECTION OF MULTI-POSITION NWP[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 392-398 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0597

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