Multi-step probability density prediction of short-term wind power based on TVFEMD-SE and YJQRG

ZHANG Wanying, HE Yaoyao, YANG Shanlin

Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (8) : 2225-2242.

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Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (8) : 2225-2242. DOI: 10.12011/SETP2021-2289

Multi-step probability density prediction of short-term wind power based on TVFEMD-SE and YJQRG

  • ZHANG Wanying, HE Yaoyao, YANG Shanlin
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Abstract

Aiming at the problem of short-term wind power multi-step prediction, a multi-step probability density prediction model is proposed based on the existing probability prediction methods, which is composed of time varying filter-based empirical mode decomposition (TVFEMD), sample entropy (SE), Yeo-Johnson transformation quantile regression (YJQR) and Gaussian kernel function. In this method, the original wind power is decomposed into a series of relatively stable components by using TVFEMD decomposition technique, and then the SE theory is applied to superimpose approximate components to reduce the task load. After that, a YJQR model is established for each reconstructed component to perform 4-step wind power prediction. The parameters of the model are comprehensively optimized by grid search to achieve the best prediction performance. Finally, the quantile prediction values of different components under each quantile are accumulated and used as the input variables of Gaussian kernel function to achieve multi-step probability density prediction of wind power. Taking the wind power data set of electrician mathematical contest in modeling (EMCM) in 2011 as an example, the results show that the proposed method achieves better multi-step prediction effects in terms of accuracy, uncertainty and reliability while guaranteeing noncrossing of quantiles.

Key words

multi-step wind power probability density prediction / time varying filter-based empirical mode decomposition / Yeo-Johnson transformation quantile regression / quantile crossing

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ZHANG Wanying , HE Yaoyao , YANG Shanlin. Multi-step probability density prediction of short-term wind power based on TVFEMD-SE and YJQRG. Systems Engineering - Theory & Practice, 2022, 42(8): 2225-2242 https://doi.org/10.12011/SETP2021-2289

References

[1] Jiang Y, Chen X Y, Yu K, et al. Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm[J]. Journal of Modern Power Systems and Clean Energy, 2017, 5(1):126-133.
[2] 卢继平,曾燕婷,喻华,等.基于改进AWNN的风电功率超短期多步预测[J].太阳能学报, 2021, 42(1):166-173.Lu J P, Zeng Y T, Yu H, et al. Ultra-short-term wind power multi-step forecasting based on improved AWNN[J]. Acta Energiae Solaris Sinica, 2021, 42(1):166-173.
[3] Zhu X X, Bowman K P, Genton M G. Incorporating geostrophic wind information for improved space-time short-term wind speed forecasting[J]. The Annals of Applied Statistics, 2014, 8(3):1782-1799.
[4] 杨茂,刘慧宇,崔杨.基于原子稀疏分解和混沌理论的风电功率超短期多步预测[J].昆明理工大学学报(自然科学版), 2019, 44(4):64-71.Yang M, Liu H Y, Cui Y. Multi-step ultra-short term wind power prediction based on atomic sparse decomposition and chaos theory[J]. Journal of Kunming University of Science and Technology (Natural Sciences), 2019, 44(4):64-71.
[5] 陈家扬,陈华,张旭.基于NWP和深度学习神经网络短期风功率预测[J].现代电子技术, 2020, 43(8):63-67.Chen J Y, Chen H, Zhang X. Short-term wind power prediction based on NWP and deep learning neural network[J]. Modern Electronics Technique, 2020, 43(8):63-67.
[6] 刘云凯,彭显刚,袁浩亮,等.基于VMD与改进QRGRU的超短期风电功率概率预测[J].电力工程技术, 2021, 40(3):72-77.Liu Y K, Peng X G, Yuan H L, et al. Ultra-short-term wind power probability prediction based on VMD and improved QRGRU[J]. Electric Power Engineering Technology, 2021, 40(3):72-77.
[7] 熊鸣.基于BP神经网络与非参数核密度估计的短期风电功率概率区间预测[J].北京信息科技大学学报(自然科学版), 2020, 35(4):51-56.Xiong M. Probability interval prediction of short-term wind power based on BP neural network and non-parametric kernel density estimation[J]. Journal of Beijing Information Science&Technology University, 2020, 35(4):51-56.
[8] 吴问足,乔颖,鲁宗相,等.风电功率概率预测方法及展望[J].电力系统自动化, 2017, 41(18):167-175.Wu W Z, Qiao Y, Lu Z X, et al. Methods and prospects for probabilistic forecasting of wind power[J]. Automation of Electric Power Systems, 2017, 41(18):167-175.
[9] 李彬,彭曙蓉,彭君哲,等.基于深度学习分位数回归模型的风电功率概率密度预测[J].电力自动化设备, 2018, 38(9):15-20.Li B, Peng S R, Peng J Z, et al. Wind power probability density forecasting based on deep learning quantile regression model[J]. Electric Power Automation Equipment, 2018, 38(9):15-20.
[10] 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 H, Meng A B, et al. Short term wind power probability density prediction based on long and short term memory network quantile regression[J]. Acta Energiae Solaris Sinica, 2021, 42(2):150-156.
[11] Zhang Z D, Qin H, Liu Y Q, et al. Wind speed forecasting based on quantile regression minimal gated memory network and kernel density estimation[J]. Energy Conversion and Management, 2019, 196:1395-1409.
[12] He Y Y, Zheng Y Y. Short-term power load probability density forecasting based on Yeo-johnson transformation quantile regression and gaussian kernel function[J]. Energy, 2018, 154:143-156.
[13] 张鑫磊,李根.基于IEEMD与LS-SVM组合的短期风电功率多步预测方法[J].电测与仪表, 2020, 57(6):52-60.Zhang X L, Li G. Multi-step prediction method of short-term wind power based on the IEEMD and LS-SVM[J]. Electrical Measurement and Instrumentation, 2020, 57(6):52-60.
[14] Berrezzek F, Khelil K, Bouadjila T. Efficient wind speed forecasting using discrete wavelet transform and artificial neural networks[J]. Revue d'Intelligence Artificielle, 2019, 33(6):447-452.
[15] 龚承柱,李兰兰,杨娟,等.基于EMD-PSR-LSSVM的城市燃气管网短期负荷预测[J].系统工程理论与实践, 2014, 34(11):3001-3008.Gong C Z, Li L L, Yang J, et al. An integrated short-term load forecasting approach for urban gas pipeline network based on EMD, PSR and LSSVM[J]. Systems Engineering-Theory&Practice, 2014, 34(11):3001-3008.
[16] 张妍,韩璞,王东风,等.基于变分模态分解和LSSVM的风电场短期风速预测[J].太阳能学报, 2018, 39(1):194-202.Zhang Y, Han P, Wang D F, et al. Short-term prediction of wind speed for wind farm based on variational mode decomposition and LSSVM model[J]. Acta Energiae Solaris Sinica, 2018, 39(1):194-202.
[17] 刘洋,谢栌乐,汪寿阳,等.基于EEMD-AWNN集成学习的中国经常账户预测研究[J].系统工程理论与实践, 2021, 41(5):1240-1251.Liu Y, Xie L L, Wang S Y, et al. Forecasting China's current account with ensemble learning approach[J]. Systems Engineering-Theory&Practice, 2021, 41(5):1240-1251.
[18] 叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报, 2011, 31(31):102-108.Ye L, Liu P. Combined model based on EMD-SVM for short-term wind power prediction[J]. Journal of Chinese Electrical Engineering Science, 2011, 31(31):102-108.
[19] Li H, Li Z, Mo W. A time varying filter approach for empirical mode decomposition[J]. Signal Processing, 2017, 138:146-158.
[20] Wang K, Fu W L, Chen T, et al. A compound framework for wind speed forecasting based on comprehensive feature selection, quantile regression incorporated into convolutional simplified long short-term memory network and residual error correction[J]. Energy Conversion and Management, 2020, 222:113234.
[21] 勾海芝,赵征,夏子涵.基于经验模式分解的神经网络组合风速预测研究[J].电力科学与工程, 2017, 33(10):62-67.Gou H Z, Zhao Z, Xia Z H. Study of combined wind speed forecasting based on empirical mode decomposition neural network[J]. Electric Power Science and Engineering, 2017, 33(10):62-67.
[22] Yeh J R, Shieh J S E, Huang N. Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2):135-156.
[23] 武昆,徐元博,杨娜.时变滤波经验模态分解与对称差分解析能量算子在轴承故障诊断中的应用[J].噪声与振动控制, 2020, 40(5):101-107.Wu K, Xu Y B, Yang N. Application of time-varying filtering empirical mode decomposition and symmetric difference analytic energy operator in fault diagnosis of bearings[J]. Noise and Vibration Control, 2020, 40(5):101-107.
[24] Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology Heart&Circulatory Physiology, 2000, 278(6):2039-2049.
[25] Cole T J, Green P J. Smoothing reference centile curves:The LMS method and penalized likelihood[J]. Statistics in Medicine, 1992, 11(10):1305-1319.
[26] Yee T. Quantile regression via vector generalized additive models[J]. Statistic in Medicine, 2004, 23:2295-2315.
[27] 何耀耀,秦杨,杨善林.基于LASSO分位数回归的中期电力负荷概率密度预测方法[J].系统工程理论与实践, 2019, 39(7):1845-1854.He Y Y, Qin Y, Yang S L. Medium-term power load probability density forecasting method based on LASSO quantile regression[J]. Systems Engineering-Theory&Practice, 2019, 39(7):1845-1854.
[28] 向玲,邓泽奇,赵#
[41].基于LPF-VMD和KELM的风速多步预测模型[J].电网技术, 2019, 43(12):4461-4467.Xiang L, Deng Z Q, Zhao Y. Multi-step wind speed prediction model based on LPF-VMD and KELM[J]. Power System Technology, 2019, 43(12):4461-4467.
[29] Khosravi A, Nahavandi S, Creighton D. Prediction interval construction and optimization for adaptive neurofuzzy inference systems[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(5):983-988.
[30] Hong T, Wang P. Fuzzy interaction regression for short term load forecasting[J]. Fuzzy Optimization&Decision Making, 2014, 13(1):91-103.
[31] Liu Y, Ye L, Qin H, et al. Middle and long-term runoff probabilistic forecasting based on Gaussian mixture regression[J]. Water Resources Management, 2019, 33(5):1785-1799.
[32] 李丹,张远航,杨保华,等.基于约束并行LSTM分位数回归的短期电力负荷概率预测方法[J].电网技术, 2021, 45(4):1356-1364.Li D, Zhang Y H, Yang B H, et al. A short time power load probabilistic forecastingmethod based on a constrained parallel-LSTM neural network quantile regression model[J]. Power System Technology, 2021, 45(4):1356-1364.

Funding

National Natural Science Foundation of China (72171068,71771073);Anhui Provincial Natural Science Foundation (2108085J36)
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