基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型

唐非

太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 735-744.

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 735-744. DOI: 10.19912/j.0254-0096.tynxb.2023-0269

基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型

  • 唐非
作者信息 +

SHORT-TERM WIND SPEED COMBINATION PREDICTION MODEL BASED ON COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND STACKING FUSION

  • Tang Fei
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文章历史 +

摘要

针对风电场短期风速预测准确度不高的问题,提出一种基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型。首先,为突出短期风速的局部特征并降低建模难度,通过互补集成经验模态分解算法将短期风速分解为若干个稳定分量。然后,利用信息熵和近似熵来判定各分量的复杂度,高复杂度分量选择最小二乘支持向量机、低复杂度分量选择随机配置网络作为对应的预测模型。利用Stacking算法对每个模型的预测值进行融合,使预测精度得到提升。最后,通过一组实际的短期风速数据作为研究对象,将提出的预测模型应用于其预测。对比结果表明,所提预测模型可提高短期风速的预测精度。

Abstract

A short-term wind speed combination prediction model based on complementary ensemble empirical mode decomposition and Stacking fusion is proposed to address the issue of low accuracy in short-term wind speed prediction in wind farms. Firstly, in order to highlight the local characteristics of short-term wind speed and reduce modeling difficulty, the short-term wind speed is decomposed into several stable components using complementary ensemble empirical mode decomposition algorithm. Then, information entropy and approximate entropy are used to determine the complexity of each component. The high complexity component selects the least squares support vector machine, and the low complexity component selects the stochastic configuration networks as the corresponding prediction model. By using the Stacking algorithm to fuse the predicted values of each model, the prediction accuracy is improved. Finally, using a group of actual short-term wind speed data set as the research object, the proposed prediction model is applied to its prediction. The comparison results indicate that the proposed prediction model improves the accuracy of short-term wind speed prediction.

关键词

风能 / 短期风速 / 组合预测 / 互补集成经验模态分解 / 多模型 / Stacking融合

Key words

wind energy / short-term wind speed / combination prediction / complementary ensemble empirical mode decomposition / multiple model / Stacking fusion

引用本文

导出引用
唐非. 基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型[J]. 太阳能学报. 2024, 45(7): 735-744 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0269
Tang Fei. SHORT-TERM WIND SPEED COMBINATION PREDICTION MODEL BASED ON COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND STACKING FUSION[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 735-744 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0269
中图分类号: TM614   

参考文献

[1] 陈吉朋, 王计安, 张雨秋, 等. 废弃风电叶片材料回收与再制造技术的研究进展[J]. 太阳能学报, 2023, 44(5): 328-335.
CHEN J P, WANG J A, ZHANG Y Q, et al.Progress on recycling methods and remanufacturing technology of waste wind turbine blades[J]. Acta energiae solaris sinica, 2023, 44(5): 328-335.
[2] CHEN H.A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy[J]. Energy reports, 2022, 8: 618-626.
[3] TIAN Z D.Preliminary research of chaotic characteristics and prediction of short-term wind speed time series[J]. International journal of bifurcation and chaos, 2020, 30(12): 2050176.
[4] 潘超, 李润宇, 蔡国伟, 等. 基于时空关联分解重构的风速超短期预测[J]. 电工技术学报, 2021, 36(22): 4739-4748.
PAN C, LI R Y, CAI G W, et al.Multi-step ultra-short-term wind speed prediction based on decomposition and reconstruction of time-spatial correlation[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4739-4748.
[5] TEMMER M, HINTERREITER J, REISS M A.Coronal hole evolution from multi-viewpoint data as input for a STEREO solar wind speed persistence model[J]. Journal of space weather and space climate, 2018, 8: A18.
[6] DO D P N, LEE Y, CHOI J. Hourly average wind speed simulation and forecast based on ARMA model in Jeju Island, Korea[J]. Journal of electrical engineering and technology, 2016, 11(6): 1548-1555.
[7] AASIM, SINGH S N, MOHAPATRA A. Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting[J]. Renewable energy, 2019, 136: 758-768.
[8] GUO T J, ZHANG L F, LIU Z K, et al.A combined strategy for wind speed forecasting using data preprocessing and weight coefficients optimization calculation[J]. IEEE access, 2020, 8: 33039-33059.
[9] HARBOLA S, COORS V.One dimensional convolutional neural network architectures for wind prediction[J]. Energy conversion and management, 2019, 195: 70-75.
[10] NATARAJAN Y J, SUBRAMANIAM NACHIMUTHU D.New SVM kernel soft computing models for wind speed prediction in renewable energy applications[J]. Soft computing, 2020, 24(15): 11441-11458.
[11] 马偲征, 王聪, 王小荣, 等. 基于混合深度学习模型的风速区间预测研究[J]. 太阳能学报, 2023, 44(3): 139-146.
MA C Z, WANG C, WANG X R, et al.Research on wind speed interval prediction based on hybrid deep learning model[J]. Acta energiae solaris sinica, 2023, 44(3): 139-146.
[12] WU J, LI N, ZHAO Y, et al.Usage of correlation analysis and hypothesis test in optimizing the gated recurrent unit network for wind speed forecasting[J]. Energy, 2022, 242: 122960.
[13] CHEN C, LIU H.Dynamic ensemble wind speed prediction model based on hybrid deep reinforcement learning[J]. Advanced engineering informatics, 2021, 48: 101290.
[14] TAN L, HAN J, ZHANG H T. Ultra-short-term wind power prediction by salp swarm algorithm-based optimizing extreme learning machine[J]. IEEE access, 2947, 8: 44470-44484.
[15] TIAN Z D, REN Y, WANG G.Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM[J]. Energy sources, part A: recovery, utilization, and environmental effects, 2019, 41(1): 26-46.
[16] SHETTY R P, SATHYABHAMA A, PAI P S.An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting[J]. Soft computing, 2021, 25(2): 1277-1295.
[17] GU B, SHEN H Q, LEI X H, et al.Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method[J]. Applied energy, 2021, 299: 117291.
[18] 臧国强, 刘晓莉, 徐颖菲, 等. 深度学习在电力设备缺陷识别中的应用进展[J]. 电气技术, 2022, 23(6): 1-7.
ZANG G Q, LIU X L, XU Y F, et al.Application progress of deep learning in power equipment defect identification[J]. Electrical engineering, 2022, 23(6): 1-7.
[19] TIAN Z D, CHEN H.A novel decomposition-ensemble prediction model for ultra-short-term wind speed[J]. Energy conversion and management, 2021, 248: 114775.
[20] TIAN Z D, LI H, LI F H.A combination forecasting model of wind speed based on decomposition[J]. Energy reports, 2021, 7: 1217-1233.
[21] TIAN Z D.Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM[J]. Engineering applications of artificial intelligence, 2020, 91: 103573.
[22] DA SILVA R G, RIBEIRO M H D M, MORENO S R, et al. A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting[J]. Energy, 2021, 216: 119174.
[23] WANG J Z, ZHANG N, LU H Y.A novel system based on neural networks with linear combination framework for wind speed forecasting[J]. Energy conversion and management, 2019, 181: 425-442.
[24] SHAO Y Y, WANG J Z, ZHANG H P, et al.An advanced weighted system based on swarm intelligence optimization for wind speed prediction[J]. Applied mathematical modelling, 2021, 100: 780-804.
[25] 杨维熙, 刘勇, 舒勤. 基于补充集合经验模态分解的短期负荷预测模型[J]. 电网技术, 2022, 46(9): 3615-3623.
YANG W X, LIU Y, SHU Q.A short-term load forecasting model based on CEEMD[J]. Power system technology, 2022, 46(9): 3615-3623.
[26] ZIVIERI R.Magnetic skyrmions as information entropy carriers[J]. IEEE transactions on magnetics, 2022, 58(1): 1500105.
[27] BAJIĆ D, JAPUNDŽIĆ-ŽIGON N. On quantization errors in approximate and sample entropy[J]. Entropy, 2021, 24(1): 73.
[28] DING M, ZHOU H, XIE H, et al.A time series model based on hybrid-kernel least-squares support vector machine for short-term wind power forecasting[J]. ISA transactions, 2021, 108: 58-68.
[29] DAI W, ZHOU X Y, LI D P, et al.Hybrid parallel stochastic configuration networks for industrial data analytics[J]. IEEE transactions on industrial informatics, 2022, 18(4): 2331-2341.
[30] MAHMOOD J, MUSTAFA G E, ALI M.Accurate estimation of tool wear levels during milling, drilling and turning operations by designing novel hyperparameter tuned models based on LightGBM and stacking[J]. Measurement, 2022, 190: 110722.
[31] WANG G, JIA L, XIAO Q.A hybrid approach based on unequal span segmentation-clustering for short-term wind power forecasting[J]. IEEE transactions on power systems, 2024, 39(1): 203-216.

基金

辽宁省教育厅科学研究项目面上项目(LJKZ0145)

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