SHORT-TERM WIND SPEED PREDICTION BASED ON FRACTAL OPTIMIZATION OF VMD-GA-BP

QuanYiming; Yu Min; Wang Wenbo; Wei Lai

doi:10.19912/j.0254-0096.tynxb.2022-0358

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (7) : 436-446. DOI: 10.19912/j.0254-0096.tynxb.2022-0358

SHORT-TERM WIND SPEED PREDICTION BASED ON FRACTAL OPTIMIZATION OF VMD-GA-BP

QuanYiming¹, Yu Min^1,2, Wang Wenbo^1,2, Wei Lai^1,2

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Abstract

In the background of the sharp reduction of traditional energy sources, there is an urgent need to propose an accurate wind speed prediction method to ensure the normal operation of power systems. This paper proposes for the first time the variational mode decomposition（VMD） and genetic algorithm （GA）based on fractal optimization. GA improved back propagation（BP） neural network model for short-term wind speed prediction. Firstly, the box-counting dimension algorithm was used to optimize the decomposition layers of VMD. Then, aiming at the non-stationarity of wind speed sequence, the original wind speed sequence was decomposed by the optimized VMD to obtain a relatively stable wind speed sub-sequence. Finally, the BP neural network improved by genetic algorithm was used to train and predict each modal component respectively, and the final prediction result was obtained by superimposing the predicted values of all components. The wind speed of a wind farm was predicted by this method, and the prediction results were compared with BP, VMD-ARMA, VMD-LSTM, VMD-BP and VMD-BP models based on fractal optimization. MAE, RMSE and MAPE were selected to evaluate the six models. The results show that the VMD-GA-BP model based on fractal optimization can significantly improve the prediction effect and reduce the wind speed prediction error.

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fractal dimension / variational mode decomposition / back propagation network / short-term wind speed prediction / genetic algorithms

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QuanYiming, Yu Min, Wang Wenbo, Wei Lai. SHORT-TERM WIND SPEED PREDICTION BASED ON FRACTAL OPTIMIZATION OF VMD-GA-BP[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 436-446 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0358

References

[1] 姜兆宇, 贾庆山, 管晓宏. 多时空尺度的风力发电预测方法综述[J]. 自动化学报, 2019, 45(1): 51-71.
JIANG Z Y, JIA Q S, GUAN X H.A review of multi-temporal-and-spatial-scale wind power forecasting method[J]. Acta automatica sinica, 2019, 45(1): 51-71.
[2] 韩自奋, 景乾明, 张彦凯, 等. 风电预测方法与新趋势综述[J]. 电力系统保护与控制, 2019, 47(24): 178-187.
HAN Z F, JING Q M, ZHANG Y K, et al.Review of wind power forecasting methods and new trends[J]. Power system protection and control, 2019, 47(24): 178-187.
[3] 冉靖, 张智刚, 梁志峰, 等. 风电场风速和发电功率预测方法综述[J]. 数理统计与管理, 2020, 39(6): 1045-1059.
RAN J, ZHANG Z G, LIANG Z F, et al.Review of wind speed and wind power prediction methods[J]. Journal of applied statistics and management, 2020, 39(6): 1045-1059.
[4] 杨茂, 张罗宾. 基于数据驱动的超短期风电功率预测综述[J]. 电力系统保护与控制, 2019, 47(13): 171-186.
YANG M, ZHANG L B.Review on ultra-short term wind power forecasting based on data-driven approach[J]. Power system protection and control, 2019, 47(13): 171-186.
[5] LEUNG D Y C, YANG Y. Wind energy development and its environmental impact: a review[J]. Renewable and sustainable energy reviews, 2012, 16(1): 1031-1039.
[6] 洪翠, 林维明, 温步瀛. 风电场风速及风电功率预测方法研究综述[J]. 电网与清洁能源, 2011, 27(1): 60-66.
HONG C, LIN W M, WEN B Y.Overview on prediction methods of wind speed and wind power[J]. Power system and clean energy, 2011, 27(1): 60-66.
[7] 易靖韬, 严欢. 基于小波分解和ARIMA-GRU混合模型的外贸风险预测预警研究[J]. 中国管理科学, 2023, 31(6): 100-110.
YI J T, YAN H.Early prediction and warning of international trade risks based on wavelet decomposition and ARIMA-GRU hybrid model[J]. Chinese journal of management science, 2023, 31(6): 100-110.
[8] 王斌, 魏成伟, 谢丽蓉, 等. 基于风速误差校正和ALO-LSSVM的风电功率预测[J]. 太阳能学报, 2022, 43(1): 58-63.
WANG B, WEI C W, XIE L R, et al.Wind power forecasting based on wind speed error correction and ALO-LSSVM[J]. Acta energiae solaris sinica, 2022, 43(1): 58-63.
[9] 向玲, 邓泽奇. 基于改进经验小波变换和最小二乘支持向量机的短期风速预测[J]. 太阳能学报, 2021, 42(2): 97-103.
XIANG L, DENG Z Q.Short-term wind speed forecasting based on improved empiricalwavelet transform and least squares support vector machines[J]. Acta energiae solaris sinica, 2021, 42(2): 97-103.
[10] 王志浩, 孙先波. 基于遗传算法优化BP神经网络的风速预测[J]. 电力设备管理, 2020(8): 128-130.
WANG Z H, SUN X B.Wind speed forecast based on genetic algorithm optimized BP neural network[J]. Electric power equipment management, 2020(8): 128-130.
[11] 马军岩, 袁逸萍, 柴同, 等. 基于组合神经网络的风机轮毂处短期风速预测[J]. 中国机械工程, 2021, 32(17): 2082-2089.
MA J Y, YUAN Y P, CHAI T, et al.Short term wind speed prediction of wind turbine hubs based on combined neural network[J]. China mechanical engineering, 2021, 32(17): 2082-2089.
[12] WANG Y, WANG J Z, LI Z W, et al.Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction[J]. Energy, 2021, 231: 121125.
[13] 田中大, 李树江, 王艳红, 等. 基于小波变换的风电场短期风速组合预测[J]. 电工技术学报, 2015, 30(9): 112-120.
TIAN Z D, LI S J, WANG Y H, et al.Short-term wind speed combined prediction for wind farms based on wavelet transform[J]. Transactions of China Electrotechnical Society, 2015, 30(9): 112-120.
[14] 高桂革, 原阔, 曾宪文, 等. 基于改进CEEMD-CS-ELM的短期风速预测[J]. 太阳能学报, 2021, 42(7): 284-289.
GAO G G, YUAN K, ZENG X W, et al.Short-term wind speed prediction based on improved CEEMD-CS-ELM[J]. Acta energiae solaris sinica, 2021, 42(7): 284-289.
[15] 金吉, 王斌, 喻敏, 等. Lorenz方程优化EMD分解过程的短期风速预测[J]. 太阳能学报, 2021, 42(6): 342-348.
JIN J, WANG B, YU M, et al.Short-term wind speed prediction based on EMD optimized by Lorenz equation[J]. Acta energiae solaris sinica, 2021, 42(6): 342-348.
[16] 王俊, 李霞, 周昔东, 等. 基于VMD和LSTM的超短期风速预测[J]. 电力系统保护与控制, 2020, 48(11): 45-52.
WANG J, LI X, ZHOU X D, et al.Ultra-short-term wind speed prediction based on VMD-LSTM[J]. Power system protection and control, 2020, 48(11): 45-52.
[17] 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.
[18] 向玲, 李京蓄, 王朋鹤,等. 基于VMD-FIG和参数优化GRU的风速多步区间预测[J]. 太阳能学报, 2021, 42(10): 237-242.
XIANG L, LI J X, WANG P H, et al.Wind speed multistep interval forecasting based on VMD-FIG and parameter-optimized GRU[J]. Acta energiae solaris sinica, 2021, 42(10): 237-242.
[19] YU X, ZHANG Z X, SONG M X.Short-term wind speed prediction of wind farms based on particle swarm optimization support vector machine[J]. IOP conference series: earth and environmental science, 2021, 804: 032061.
[20] SHAO B L, SONG D, BIAN G Q, et al.Wind speed forecast based on the LSTM neural network optimized by the firework algorithm[J]. Advances in materials science and engineering, 2021, 2021: 4874757.
[21] 陈臣鹏, 赵鑫, 毕贵红, 等. 基于Kmeans-VMD-LSTM的短期风速预测[J]. 电机与控制应用, 2021, 48(12): 85-93.
CHEN C P, ZHAO X, BI G H, et al.Short-term wind speed prediction based on Kmeans-VMD-LSTM[J]. Electric machines & control application, 2021, 48(12): 85-93.
[22] GUSSO A, MELLO L E.Fractal dimension of basin boundaries calculated using the basin entropy[J]. Chaos, solitons & fractals, 2021, 153: 111532.
[23] RI S.A remarkable fact for the box dimensions of fractal interpolation curves of R³[J]. Chaos, solitons & fractals, 2021, 151: 111205.
[24] TIAN L.The estimates of holder index and the box dimension for the hadamard fractional integral[J]. Fractals, 2021, 29(3): 2150072.
[25] PEREIRA D S L A, ZIELINSKI K M C, RODRIGUES E O, et al. A novel approach to estimated Boulingand-Minkowski fractal dimension from complex networks[J]. Chaos, solitons & fractals, 2022, 157: 111894.