IMPROVED WEIGHTED GRU WIND POWER INTERVAL PREDICTION BASED ON QUANTILE REGRESSION

Liu Tianhong; Qi Shengli; Yi Yang; Jian Libin; Qiao Xianzhu; Zhang Enze

doi:10.19912/j.0254-0096.tynxb.2023-1311

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 291-298. DOI: 10.19912/j.0254-0096.tynxb.2023-1311

IMPROVED WEIGHTED GRU WIND POWER INTERVAL PREDICTION BASED ON QUANTILE REGRESSION

Liu Tianhong¹, Qi Shengli¹, Yi Yang¹, Jian Libin², Qiao Xianzhu¹, Zhang Enze¹

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Abstract

In order to improve the accuracy of short-term wind power prediction, an improved entropy weighted GRU wind power interval prediction model based on quantile regression （QR-EGRU） is proposed to solve the problems that point prediction is difficult to describe wind power uncertainty and the traditional GRU cannot accurately track the data mutation when data changes. Firstly, the improved adaptive wavelet threshold denoising method is used to reduce the noise of the original data. Then two update gate weight matrices are introduced to replace the traditional update gate weight matrix. The new weight matrices adopt information entropy to dynamically adjust the matrix changes, quantify the change degree of the weights, and construct the information entropy weighted GRU（EGRU） network. Finally, the probability interval under different quantiles of the point prediction is obtained based on quantile regression algorithm. Experimental results show that the proposed model can improve the prediction accuracy compared with other comparison methods under the same experimental conditions and has a better interval prediction performance.

Key words

wind power / prediction / gated recurrent unit / quantile regression / information entropy / wavelet threshold denoising

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Liu Tianhong, Qi Shengli, Yi Yang, Jian Libin, Qiao Xianzhu, Zhang Enze. IMPROVED WEIGHTED GRU WIND POWER INTERVAL PREDICTION BASED ON QUANTILE REGRESSION[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 291-298 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1311

References

[1] 乔颖, 鲁宗相, 闵勇. 提高风电功率预测精度的方法[J]. 电网技术, 2017, 41(10): 3261-3269.
QIAO Y, LU Z X, MIN Y.Research & application of raising wind power prediction accuracy[J]. Power system technology, 2017, 41(10): 3261-3269.
[2] 黎静华, 骆怡辰, 杨舒惠, 等. 可再生能源电力不确定性预测方法综述[J]. 高电压技术, 2021, 47(4):1144-1157.
LI J H, LUO Y C, YANG S H, et al.Review of uncertainty forecasting methods for renewable energy power[J]. High voltage engineering, 2021, 47(4): 1144-1157.
[3] HU J M, HENG J N, WEN J M, et al.Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm[J]. Renewable energy, 2020, 162: 1208-1226.
[4] ZHANG C, JI C L, HUA L, et al.Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction[J]. Renewable energy, 2022, 197: 668-682.
[5] LIU G J, WANG Y, QIN H, et al.Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method[J]. Renewable energy, 2023, 209: 231-247.
[6] 韩丽, 乔妍, 景惠甜. 基于误差分类的风电功率区间评估[J]. 电力系统自动化, 2021, 45(1): 97-104.
HAN L, QIAO Y, JING H T.Interval estimation of wind power based on error classification[J]. Automation of electric power systems, 2021, 45(1): 97-104.
[7] 刘芳, 潘毅, 刘辉, 等. 风电功率预测误差分段指数分布模型[J]. 电力系统自动化, 2013, 37(18): 14-19.
LIU F, PAN Y, LIU H, et al.Piecewise exponential distribution model of wind power forecasting error[J]. Automation of electric power systems, 2013, 37(18): 14-19.
[8] 殷豪, 黄圣权, 孟安波, 等. 基于长短期记忆网络分位数回归的短期风电功率概率密度预测[J]. 太阳能学报, 2021, 42(2): 150-156.
YIN H, HUANG S Q, MENG A B, et al.Short-term wind power probability density prediction based on long short term memory network quantile regression[J]. Acta energiae solaris sinica, 2021, 42(2): 150-156.
[9] ZHANG C, WEI H K, ZHAO X, et al.A Gaussian process regression based hybrid approach for short-term wind speed prediction[J]. Energy conversion and management, 2016, 126: 1084-1092.
[10] 余沣, 董存, 王铮, 等. 考虑山东近海不同风能天气特征的风电功率区间预测模型[J]. 电网技术, 2020, 44(4): 1238-1247.
YU F, DONG C, WANG Z, et al.Wind power interval forecasting model considering different wind energy weather characteristics in Shandong offshore areas[J]. Power system technology, 2020, 44(4): 1238-1247.
[11] 王晓东, 鞠邦国, 刘颖明, 等. 基于QR-NFGLSTM与核密度估计的风电功率概率预测[J]. 太阳能学报, 2022, 43(2): 479-485.
WANG X D, JU B G, LIU Y M, et al.Probability prediction of wind power based on QR-NFGLSTM and kernel density estimation[J]. Acta energiae solaris sinica, 2022, 43(2): 479-485.
[12] ZHAO Z N, YUN S N, JIA L Y, et al.Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features[J]. Engineering applications of artificial intelligence, 2023, 121: 105982.
[13] 张冬梅, 李金平, 李江, 等. 基于门控权重单元的多变量时间序列预测[J]. 湖南大学学报(自然科学版), 2021, 48(10): 105-112.
ZHANG D M, LI J P, LI J, et al.Multivariate time series prediction based on gating weight unit[J]. Journal of Hunan University (natural sciences), 2021, 48(10): 105-112.
[14] 邬超, 朱桂萍, 钱敏慧. 基于信息熵的历史数据选取对超短期风电功率预测精度影响研究[J]. 电网技术, 2021, 45(5): 1767-1772.
WU C, ZHU G P, QIAN M H.Impact of historical data selection on accuracy of ultra-short-term wind power prediction based on prediction information entropy[J]. Power system technology, 2021, 45(5): 1767-1772.
[15] DONOHO D L, JOHNSTONE I M.Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
[16] WANG F Y, CEN J, YU Z W, et al.Research on a hybrid model for cooling load prediction based on wavelet threshold denoising and deep learning: a study in China[J]. Energy reports, 2022, 8: 10950-10962.
[17] TAYLOR J W.A quantile regression neural network approach to estimating the conditional density of multiperiod returns[J]. Journal of forecasting, 2000, 19(4): 299-311.
[18] Wind Power Forecasting [OB/OL]https://www.kaggle.com/datasets/theforcecoder/wind-power-forecasting.
[19] LIU B D, NOWOTARSKI J, HONG T, et al.Probabilistic load forecasting via quantile regression averaging on sister forecasts[J]. IEEE transactions on smart grid, 2017, 8(2): 730-737.
[20] 李雪莹, 李宗民, 陈光源, 等. 小波变换的潮滩沉积物含水量预测[J]. 光谱学与光谱分析, 2022, 42(4): 1156-1161.
LI X Y, LI Z M, CHEN G Y, et al.Prediction of tidal flat sediment moisture content based on wavelet transform[J]. Spectroscopy and spectral analysis, 2022, 42(4): 1156-1161.
[21] LIU W J, JIA L.Wind power prediction based on the stacking model of XGBoost and random forest[C]//2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS). Chengdu, China, 2022: 1118-1123.