为提高超短期风功率预测精度,提出一种基于SGMD-SE与优化的TCN-BiLSTM/BiGRU组合预测模型。首先,采用最大互信息系数（MIC）选取出风功率强相关变量,作为预测模型的输入特征。其次,利用能抑制模态混叠、无须设置分解参数的辛几何模态分解（SGMD）,将原始风功率信号分解成若干个较平稳的初始辛几何分量（SGC）。然后,使用样本熵（SE）完成初始分量重构并将重构后分量划分为复杂度高、低两类,根据两类分量不同特点,分别搭建TCN-BiLSTM模型、TCN-BiGRU模型进行预测。为改善BiLSTM、BiGRU预测性能,采用时间卷积网络（TCN）提取两类分量特征,并提出一种基于Tent混沌映射和柯西变异的改进鱼鹰优化算法（IOOA）优化其关键参量。最后,叠加各分量预测值得到最终的预测结果。结果表明：所提出的组合预测模型可有效提升超短期风功率预测的准确率,具有较强的实用价值。
Abstract
To enhance the precision of ultra-short-term wind power prediction, a model is proposed that combines SGMD-SE with an optimized TCN-BiLSTM/BiGRU framework. Firstly, highly correlated variables are selected using maximum information coefficient （MIC） as input features. Secondly, symplectic geometry modal decomposition （SGMD） is utilized to decompose the original signal into initial symplectic geometry components （SGC）, effectively suppressing mode mixing and eliminating the need for decomposition parameters. Then, sample entropy （SE） is applied to reconstruct the initial components, which are classified into high and low complexity categories. Based on the different characteristics of these categories, separate TCN-BiLSTM and TCN-BiGRU models are built for prediction. To improve the predictive performance of BiLSTM and BiGRU, time convolutional networks （TCN） are utilized to extract features from the two component categories.Additionally,an improved optimization algorithm called IOOA, based on Tent chaotic mapping and Cauchy mutation, is proposed to optimize their key parameters. Finally, the final prediction result is superimposed by combining the predicted values of each component. The findings suggest that the proposed hybrid prediction model significantly enhances the accuracy of ultra-short-term wind power forecasting and holds substantial practical utility.
关键词
风功率预测 /
分解 /
长短期记忆网络 /
时间卷积网络 /
鱼鹰优化算法
Key words
wind power prediction /
decomposition /
long short-term memory /
time convolutional network /
osprey optimization algorithm
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 赵宇洋, 赵浩然, 谭建鑫, 等. 面向风电制氢的超短期组合功率预测[J]. 太阳能学报, 2023, 44(3): 162-168.
ZHAO Y Y, ZHAO H R, TAN J X, et al.Combined ultra-short-term power prediction for wind power hydrogen production technology[J]. Acta energiae solaris sinica, 2023, 44(3): 162-168.
[2] LIU H, MI X W, LI Y F.Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM[J]. Energy conversion and management, 2018, 159: 54-64.
[3] 宋江涛, 崔双喜, 刘洪广. 基于二次分解NGO-VMD残差项与长短时记忆神经网络的超短期风功率预测[J]. 科学技术与工程, 2023, 23(6): 2428-2437.
SONG J T, CUI S X, LIU H G.Ultra-short-term wind power prediction based on the quadratic decomposition NGO-VMD residual term and LSTM[J]. Science technology and engineering, 2023, 23(6): 2428-2437.
[4] 李丽, 叶林. 基于改进持续法的短期风电功率预测[J]. 农业工程学报, 2010, 26(12): 182-187.
LI L, YE L.Short-term wind power forecasting based on an improved persistence approach[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(12): 182-187.
[5] 曹敏, 程东年, 张建辉, 等. 基于自适应阈值的网络流量异常检测算法[J]. 计算机工程, 2009, 35(19): 164-166, 177.
CAO M, CHENG D N, ZHANG J H, et al.Network traffic abnormality detection algorithm based on self-adaptive threshold[J]. Computer engineering, 2009, 35(19): 164-166, 177.
[6] 葛乐, 陆文伟, 袁晓冬, 等. 基于改进相似日和ABC-SVM的光伏电站功率预测[J]. 太阳能学报, 2018, 39(3): 775-782.
GE L, LU W W, YUAN X D, et al.Power forecasting of photovoltaic plant based on improved similar day and abc-svm[J]. Acta energiae solaris sinica, 2018, 39(3): 775-782.
[7] SINGH S, BANSAL P, HOSEN M, et al.Forecasting annual natural gas consumption in USA: application of machine learning techniques- ANN and SVM[J]. Resources policy, 2023, 80: 103159.
[8] ADRIANO DE MELO G, SUGIMOTO D N, TASINAFFO P M, et al. A new approach to river flow forecasting: LSTM and GRU multivariate models[J]. IEEE Latin America transactions, 2019, 17(12): 1978-1986.
[9] 陈峰, 余轶, 徐敬友, 等. 基于Bayes-LSTM网络的风电出力预测方法[J]. 电力系统保护与控制, 2023, 51(6): 170-178.
CHEN F, YU Y, XU J Y, et al.Prediction method of wind power output based on a Bayes-LSTM network[J]. Power system protection and control, 2023, 51(6): 170-178.
[10] 董雪, 赵宏伟, 赵生校, 等. 基于SOM聚类和二次分解的BiGRU超短期光伏功率预测[J]. 太阳能学报, 2022, 43(11): 85-93.
DONG X, ZHAO H W, ZHAO S X, et al.Ultra-short-term forecasting method of photovoltaic power based on SOM clustering, secondary decomposition and bigru[J]. Acta energiae solaris sinica, 2022, 43(11): 85-93.
[11] SAJJAD M, AHMAD KHAN Z, ULLAH A, et al.A novel CNN-GRU-based hybrid approach for short-term residential load forecasting[J]. IEEE access, 2020, 8: 143759-143768.
[12] 倪超, 王聪, 朱婷婷, 等. 基于CNN-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2022, 43(3): 197-202.
NI C, WANG C, ZHU T T, et al.Super-short-term forecast of solar irradiance based on CNN-Bi-LSTM[J]. Acta energiae solaris sinica, 2022, 43(3): 197-202.
[13] 胡威, 张新燕, 李振恩, 等. 基于优化的VMD-mRMR-LSTM模型的短期负荷预测[J]. 电力系统保护与控制, 2022, 50(1): 88-97.
HU W, ZHANG X Y, LI Z E, et al.Short-term load forecasting based on an optimized VMD-mRMR-LSTM model[J]. Power system protection and control, 2022, 50(1): 88-97.
[14] RESHEF D N, RESHEF Y A, FINUCANE H K, et al.Detecting novel associations in large data sets[J]. Science, 2011, 334(6062): 1518-1524.
[15] 程正阳, 王荣吉, 潘海洋. 辛几何模态分解方法及其分解能力研究[J]. 振动与冲击, 2020, 39(13): 27-35.
CHENG Z Y, WANG R J, PAN H Y.Symplectic geometry mode decomposition method and its decomposition ability[J]. Journal of vibration and shock, 2020, 39(13): 27-35.
[16] BONIZZI P, KAREL J M H, MESTE O, et al. Singular spec-trum decomposition: a new method for time series decomposition[J]. Advances in adaptive data analysis, 2014, 6(4): 1450011.
[17] 李云峰, 高云鹏, 蔡星月, 等. 自适应辛几何模态分解和短时能量差分因子在电能质量扰动检测中的应用[J]. 电工技术学报, 2022, 37(17): 4390-4400.
LI Y F, GAO Y P, CAI X Y, et al.Application of adaptive symplectic geometry modal decomposition and short-time energy difference factor in power quality disturbance detection[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4390-4400.
[18] 郑近德, 陈敏均, 程军圣, 等. 多尺度模糊熵及其在滚动轴承故障诊断中的应用[J]. 振动工程学报, 2014, 27(1): 145-151.
ZHENG J D, CHEN M J, CHENG J S, et al.Multiscale fuzzy entropy and its application in rolling bearing fault diagnosis[J]. Journal of vibration engineering, 2014, 27(1): 145-151.
[19] BAI S J, KOLTER J Z, KOLTUN V, et al.An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL]. http://arxiv.org/abs/1803.01271v2
[20] DEHGHANI M, TROJOVSKÝ P.Osprey optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems[J]. Frontiers in mechanical engineering, 2023, 8: 1126450.
基金
国家自然科学基金（52067020）; 新疆维吾尔自治区重点研发项目（2022B01019-1）
PDF(1271 KB)
