计及误差信息的自适应超短期风速预测模型

张金良; 刘子毅; 孙安黎

doi:10.19912/j.0254-0096.tynxb.2022-1692

PDF(2161 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (3) : 18-28. DOI: 10.19912/j.0254-0096.tynxb.2022-1692

计及误差信息的自适应超短期风速预测模型

张金良¹, 刘子毅¹, 孙安黎²

作者信息 +

ADAPTIVE ULTRA-SHORT-TERM WIND SPEED PREDICTION MODEL CONSIDERING ERROR INFORMATION

Zhang Jinliang¹, Liu Ziyi¹, Sun Anli²

Author information +

文章历史 +

摘要

为提升超短期风速预测精度,提出一种计及误差信息的自适应混合预测模型。应用自适应噪声的完备集合经验模态分解模型与鲸鱼优化的变分模态分解模型分别对风速样本数据与预测误差进行分解,同时计算各子序列的模糊熵以判断序列复杂程度。在此基础上,应用鲸鱼优化的长短期网络预测复杂程度较高的序列,差分自回归移动平均模型预测复杂程度较低的序列。最后,将初始风速预测结果和风速误差预测值相加得到基于误差修正的超短期风速预测值。结果表明,修正预测误差与考虑分解策略可有效提升点预测的性能,与基准模型相比,所提模型在多场景下均具备优良的预测精度。

Abstract

Wind speed is nonlinear and non-stationary. In order to improve the prediction accuracy of ultra short term wind speed, an adaptive hybrid prediction model with error correction is proposed. A fully ensemble empirical modal decomposition model with adaptive noise and an improved variational modal decomposition model are used to decompose the sample data series and the future prediction error series respectively, while the fuzzy entropy of each sub-series is calculated to determine the complexity of the series. Further, the improved long and short term network is applied to predict the higher complexity series and the autoregressive integrated moving average model to predict the lower complexity series. Finally, the prediction results and the wind speed error prediction values are summed to obtain the error-corrected ultra-short-term wind speed prediction values. The results show that the correction of forecast errors and double decomposition can effectively improve the performance of point prediction, and the proposed model has excellent prediction accuracy in multiple scenarios compared with the benchmark model.

导出引用

张金良, 刘子毅, 孙安黎. 计及误差信息的自适应超短期风速预测模型[J]. 太阳能学报. 2024, 45(3): 18-28 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1692

Zhang Jinliang, Liu Ziyi, Sun Anli. ADAPTIVE ULTRA-SHORT-TERM WIND SPEED PREDICTION MODEL CONSIDERING ERROR INFORMATION[J]. Acta Energiae Solaris Sinica. 2024, 45(3): 18-28 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1692

中图分类号： TM614

参考文献

[1] JUNG C, TAUBERT D, SCHINDLER D.The temporal variability of global wind energy:long-term trends and inter-annual variability[J]. Energy conversion and management, 2019, 188: 462-472.
[2] PRADHAN C, BHENDE C N.Online load frequency control in wind integrated power systems using modified Jaya optimization[J]. Engineering applications of artificial intelligence, 2019, 77: 212-228.
[3] ZHANG G, YANG D Z, GALANIS G, et al.Solar forecasting with hourly updated numerical weather prediction[J]. Renewable and sustainable energy reviews, 2022, 154: 111768.
[4] WANG H, HAN S A, LIU Y Q, et al.Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system[J]. Applied energy, 2019, 237: 1-10.
[5] DEPPE A J, GALLUS W A Jr, TAKLE E S. A WRF ensemble for improved wind speed forecasts at turbine height[J]. Weather and forecasting, 2013, 28(1): 212-228.
[6] 丁藤, 冯冬涵, 林晓凡, 等. 基于修正后ARIMA-GARCH模型的超短期风速预测[J]. 电网技术, 2017, 41(6): 1808-1814.
DING T, FENG D H, LIN X F, et al.Ultra-short-term wind speed forecasting based on improved ARIMA-GARCH model[J]. Power system technology, 2017, 41(6): 1808-1814.
[7] 姜言, 黄国庆, 彭新艳, 等. 基于GARCH的短时风速预测方法[J]. 西南交通大学学报, 2016, 51(4): 663-669, 742.
JIANG Y, HUANG G Q, PENG X Y, et al.Method of short-term wind speed forecasting based on generalized autoregressive conditional heteroscedasticity model[J]. Journal of Southwest Jiaotong University, 2016, 51(4): 663-669, 742.
[8] 岳勇, 陈雯婷, 聂伟. LMD和ARMA组合风速预测方法[J]. 中国测试, 2020, 46(8): 126-130.
YUE Y, CHEN W T, NIE W.Composite wind speed prediction method based on LMD and ARMA[J]. China measurement & test, 2020, 46(8): 126-130.
[9] 赵征, 王晓亮, 张亚刚. 基于ARMA-ARCH模型和BP模型的短期风速组合预测方法研究[J]. 电网与清洁能源, 2018, 34(11): 45-51.
ZHAO Z, WANG X L, ZHANG Y G.Short-term wind speed combined prediction based on ARMA-ARCH model and BP model[J]. Power system and clean energy, 2018, 34(11): 45-51.
[10] 张建平, 于新建, 陈栋, 等. 随机算法改进的RCSA-ANN模型及近海短期风速预测[J]. 空气动力学学报, 2022, 40(4): 110-116.
ZHANG J P, YU X J, CHEN D, et al.An improved RCSA-ANN model for the prediction of offshore short-term wind speed[J]. Acta aerodynamica sinica, 2022, 40(4): 110-116.
[11] 王硕禾, 张嘉姗, 陈祖成, 等. 基于改进深度信念网络的风电场短期风速预测[J]. 可再生能源, 2020, 38(11): 1489-1494.
WANG S H, ZHANG J S, CHEN Z C, et al.Short-term wind speed forecasting of wind farm based on improved deep belief network[J]. Renewable energy resources, 2020, 38(11): 1489-1494.
[12] 李昊. 基于变分模态分解和回声状态网络的风速时间序列预测[D]. 沈阳: 沈阳工业大学, 2021.
LI H.Wind speed time series prediction based on variational mode decomposition and echo state network[D]. Shenyang: Shenyang University of Technology, 2021.
[13] 袁咪咪, 宫法明, 李昕. 基于CNN-LSTM的多因素时空风速预测[J]. 计算机系统应用, 2021, 30(8): 133-141.
YUAN M M, GONG F M, LI X.Multifactor spatio-temporal wind speed prediction based on CNN-LSTM[J]. Computer systems & applications, 2021, 30(8): 133-141.
[14] 关永锋, 喻敏, 胡佳. 基于自适应组合模型的超短期风速预测[J]. 电力系统保护与控制, 2022, 50(4): 120-128.
GUAN Y F, YU M, HU J.Ultra-short-term wind speed prediction based on an adaptive integrated model[J]. Power system protection and control, 2022, 50(4): 120-128.
[15] 李大中, 李昉, 张克延. 基于变量选择深度信念神经网络的风速预测[J]. 华北电力大学学报(自然科学版), 2021, 48(1): 62-68.
LI D Z, LI F, ZHANG K Y.Wind speed prediction based on DBN with variable selection[J]. Journal of North China Electric Power University (natural science edition), 2021, 48(1): 62-68.
[16] 朱昶胜, 李岁寒. 基于改进果蝇优化算法的随机森林回归模型及其在风速预测中的应用[J]. 兰州理工大学学报, 2021, 47(4): 83-90.
ZHU C S, LI S H.Random forest regression model based on improved fruit fly optimization algorithm and its application in wind speed forecasting[J]. Journal of Lanzhou University of Technology, 2021, 47(4): 83-90.
[17] 孙斌, 姚海涛. 基于PSO优化LSSVM的短期风速预测[J]. 电力系统保护与控制, 2012, 40(5): 85-89.
SUN B, YAO H T.The short-term wind speed forecast analysis based on the PSO-LSSVM predict model[J]. Power system protection and control, 2012, 40(5): 85-89.
[18] 陈忠. 基于BP神经网络与遗传算法风电场超短期风速预测优化研究[J]. 可再生能源, 2012, 30(2): 32-36.
CHEN Z.Optimazation study on ultra-short term wind speed forecasting of wind farms based on BP neural network and genetic algorithm[J]. Renewable energy resources, 2012, 30(2): 32-36.
[19] BOKDE N, FEIJÓO A, VILLANUEVA D, et al. A review on hybrid empirical mode decomposition models for wind speed and wind power prediction[J]. Energies, 2019, 12(2): 254.
[20] 陈臣鹏, 赵鑫, 毕贵红, 等. 基于多模式分解和麻雀优化残差网络的短期风速预测模型[J]. 电网技术, 2022, 46(8): 2975-2985.
CHEN C P, ZHAO X, BI G H, et al.SSA-res-GRU short-term wind speed prediction model based on multi-model decomposition[J]. Power system technology, 2022, 46(8): 2975-2985.
[21] 桑茂景, 谢丽蓉, 李进卫, 等. 基于LMD-IMVO-LSSVM的短期风速预测[J]. 可再生能源, 2021, 39(9): 1197-1203.
SANG M J, XIE L R, LI J W, et al.Short-term wind speed prediction based on LMD-IMVO-LSSVM[J]. Renewable energy resources, 2021, 39(9): 1197-1203.
[22] 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.
[23] ZHANG J L, WEI Y M, TAN Z F.An adaptive hybrid model for short term wind speed forecasting[J]. Energy, 2020, 190: 115615.
[24] 李秉晨, 于惠钧, 丁华轩, 等. 基于CEEMD和LSTM-ARIMA的短期风速预测[J]. 中国测试, 2022, 48(2): 163-168.
LI B C, YU H J, DING H X, et al.Short-term wind speed prediction based on CEEMD and LSTM-ARIMA[J]. China measurement & test, 2022, 48(2): 163-168.
[25] 王秀. 基于WD-ARIMA-LSTM的短期风速预测[D]. 北京: 华北电力大学, 2021.
WANG X.Short-term wind speed prediction based on WD-ARIMA-LSTM[D]. Beijing: North China Electric Power University, 2021.
[26] TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al.A complete ensemble empirical mode decomposition with adaptive noise[C]//2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague, Czech Republic, 2011: 4144-4147.
[27] 孟凡煦. 基于变分模态分解和极限学习机的短期负荷预测方法研究[D]. 大庆: 东北石油大学, 2019.
MENG F X.Research on A forecasting method of short term load based on VMD and ELM[D]. Daqing: Northeast Petroleum University, 2019.
[28] 史加荣, 赵丹梦, 王琳华, 等. 基于RR-VMD-LSTM的短期风电功率预测[J]. 电力系统保护与控制, 2021, 49(21): 63-70.
SHI J R, ZHAO D M, WANG L H, et al.Short-term wind power prediction based on RR-VMD-LSTM[J]. Power system protection and control, 2021, 49(21): 63-70.