针对风速的波动性和随机性等特点,提出一种基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测方法。该方法的基本思路是构造基于变分模态分解、样本熵和奇异谱分析的二次分解的方法,将原始风速序列分解为不同的子序列,并对这些子序列分别建立预测模型,最后重构。对变分模态分解的子序列建立基于长短时记忆网络的深度学习模型预测,而残差序列进行二次分解后的子序列建立乌鸦搜索算法优化的组合预测模型预测。最后,对子序列进行重构并得到最终的预测结果。使用实际的风速观测资料开展模拟实验,结果表明：在3个风电场中,所提模型与其他模型相比平均相对误差分别提升了30.07%、37.56%和37.40%,验证了混合模型在超短期风速预测中的有效性和稳定性,以及在不同数据集上的泛化性能。
Abstract
Aiming at the characteristics of wind speed series such as volatility and randomness, this study develops an ultra-short-term wind speed forecasting method based on an optimal combination model of secondary decomposition and crow search algorithm. The basic idea is to construct a secondary decomposition process based on variational mode decomposition, sample entropy and singular spectrum analysis, which is applied to decompose the original wind speed series into a set of subcomponents. After this, establish a prediction model for each subcomponent. Specifically, for the subcomponents of variational mode decomposition, a deep learning model based on long short-term memory network is applied, and for the subcomponents of residual sequence after secondary decomposition, a combination forecasting model optimized by crow search algorithm is designed. The final prediction is obtained by reconstructing the predictions of all subcomponents. Using real observation datasets of wind speed for model simulation, the developed method decreases MAPE by 19.76%, 24.91%, and 26.36% compared with several other models, signifying the effectiveness and stability of the developed method for ultra-short-term wind speed forecasting as well as a good generalization performance on different datasets.
关键词
风速 /
预测 /
长短时记忆 /
二次分解 /
乌鸦搜索算法 /
组合预测模型
Key words
wind speed /
forecasting /
long short-term memory /
secondary decomposition /
crow search algorithm /
combination forecasting model
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参考文献
[1] 孙舶皓, 汤涌, 叶林, 等. 基于分层分布式模型预测控制的多时空尺度协调风电集群综合频率控制策略[J]. 中国电机工程学报, 2019, 39(1): 155-167, 330.
SUN B H, TANG Y, YE L, et al.Integrated frequency control strategy for wind power cluster with multiple temporal-spatial scale coordination based on H-DMPC[J]. Proceedings of the CSEE, 2019, 39(1): 155-167, 330.
[2] NAIK J, BISOI R, DASH P K.Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression[J]. Renewable energy, 2018, 129: 357-383.
[3] 赵泽妮, 云斯宁, 贾凌云, 等. 基于统计模型的短期风能预测方法研究进展[J]. 太阳能学报, 2022, 43(11): 224-234.
ZHAO Z N, YUN S N, JIA L Y, et al.Recent progress in short-term forecasting of wind energy based on statistical models[J]. Acta energiae solaris sinica, 2022, 43(11): 224-234.
[4] ALLEN D J, TOMLIN A S, BALE C S E, et al. A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data[J]. Applied energy, 2017, 208: 1246-1257.
[5] LIANG T, XIE G F, FAN S R, et al.A combined model based on CEEMDAN, permutation entropy, gated recurrent unit network, and an improved bat algorithm for wind speed forecasting[J]. IEEE access, 2020, 8: 165612-165630.
[6] XIAO L, DONG Y X, DONG Y.An improved combination approach based on Adaboost algorithm for wind speed time series forecasting[J]. Energy conversion and management, 2018, 160: 273-288.
[7] WU C Y, WANG J Z, CHEN X J, et al.A novel hybrid system based on multi-objective optimization for wind speed forecasting[J]. Renewable energy, 2020, 146: 149-165.
[8] HE X H, NIE Y, GUO H L, et al.Research on a novel combination system on the basis of deep learning and swarm intelligence optimization algorithm for wind speed forecasting[J]. IEEE access, 2020, 8: 51482-51499.
[9] ZHANG Y, LI Y T, ZHANG G Y.Short-term wind power forecasting approach based on Seq2Seq model using NWP data[J]. Energy, 2020, 213: 118371.
[10] 陆冰鉴, 周鹏, 王兴, 等. 基于EEMD和LSTM的短期风速预测模型研究[J]. 软件工程, 2020, 23(3): 43-48.
LU B J, ZHOU P, WANG X, et al.Research on short-term wind speed forecasting model based on EEMD and LSTM[J]. Software engineering, 2020, 23(3): 43-48.
[11] DONG Y X, WANG J, XIAO L, et al.Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target[J]. Energy, 2021, 215: 119180.
[12] CHEN Y R, DONG Z K, WANG Y, et al.Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history[J]. Energy conversion and management, 2021, 227: 113559.
[13] 向玲, 刘佳宁, 苏浩, 等. 基于CEEMDAN二次分解和LSTM的风速多步预测研究[J]. 太阳能学报, 2022, 43(8): 334-339.
XIANG L, LIU J N, SU H, et al.Research on multi-step wind speed forecast based on ceemdan secondary decomposition and lstm[J]. Acta energiae solaris sinica, 2022, 43(8): 334-339.
[14] 赵鑫, 陈臣鹏, 毕贵红, 等. 基于PAM-SSD-LSTM的短期风速预测[J]. 太阳能学报, 2023, 44(1): 281-288.
ZHAO X, CHEN C P, BI G H, et al.Short-term wind speed prediction based on PAM-SSD-LSTM[J]. Acta energiae solaris sinica, 2023, 44(1): 281-288.
[15] BATES J M, GRANGER C W J. The combination of forecasts[J]. Journal of the Operational Research Society, 1969, 20(4): 451-468.
[16] XIAO L, WANG J Z, DONG Y, et al.Combined forecasting models for wind energy forecasting: a case study in China[J]. Renewable and sustainable energy reviews, 2015, 44: 271-288.
[17] 林涛, 刘航鹏, 赵参参, 等. 基于SSA-PSO-ANFIS的短期风速预测研究[J]. 太阳能学报, 2021, 42(3): 128-134.
LIN T, LIU H P, ZHAO S S, et al.Short-term wind speed prediction based on SSA-PSO-ANFIS[J]. Acta energiae solaris sinica, 2021, 42(3): 128-134.
[18] SONG J J, WANG J Z, LU H Y.A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting[J]. Applied energy, 2018, 215: 643-658.
[19] 蔺琳, 王万雄. 基于EMD-GWO-SVR组合模型的短期风速预测[J]. 电子科技, 2023, 36(5): 1-8.
LIN L, WANG W X.Short-term wind speed prediction based on EMD-GWO-SVR combined model[J]. Electronic science and technology, 2023, 36(5): 1-8.
[20] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[21] LIU H, TIAN H Q, LIANG X F, et al.Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks[J]. Applied energy, 2015, 157: 183-194.
[22] 颉清云, 宋连庆, 何永康. 基于经验模态二次分解的改进门控循环单元风速超短期组合预测模型[J]. 国外电子测量技术, 2021, 40(6): 6-13.
XIE Q Y, SONG L Q, HE Y K.Ultra-short-term combined forecasting model for wind speed of improved gated recurrent unit based on empirical mode quadratic decomposition[J]. Foreign electronic measurement technology, 2021, 40(6): 6-13.
[23] 赵征, 于悦波. 基于二次分解与IGWO-LSSVR模型的超短期风速预测研究[J]. 电力科学与工程, 2021, 37(5): 18-25.
ZHAO Z, YU Y B.Research on ultra-short-term wind speed prediction based on twice decomposition and IGWO-LSSVR model[J]. Electric power science and engineering, 2021, 37(5): 18-25.
[24] RICHMAN J S, MOORMAN J R.Physiological time-series analysis using approximate entropy and sample entropy[J]. American journal of physiology heart and circulatory physiology, 2000, 278(6): H2039-H2049.
[25] ZHANG Y G, PAN G F.A hybrid prediction model for forecasting wind energy resources[J]. Environmental science and pollution research, 2020, 27(16): 19428-19446.
[26] DONG Q L, SUN Y H, LI P Z.A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: a case study of wind farms in China[J]. Renewable energy, 2017, 102: 241-257.
[27] ASKARZADEH A.A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm[J]. Computers & structures, 2016, 169: 1-12.
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