郭晓鹏, 赵琪, 张国维. Multi-step Prediction of Wind Power Based on Improved Variational Modal Decomposition and Informer Hybrid Model[J]. 2026, 43(1): 20-29.
DOI:
郭晓鹏, 赵琪, 张国维. Multi-step Prediction of Wind Power Based on Improved Variational Modal Decomposition and Informer Hybrid Model[J]. 2026, 43(1): 20-29. DOI: 10.19725/j.cnki.1007-2322.2023.0429.
Multi-step Prediction of Wind Power Based on Improved Variational Modal Decomposition and Informer Hybrid Model
The accurate predition of wind power is crucial for enhancing the efficiency of wind energy utilization and achieving sustainable development of the power system. In view of this
a multi-step wind power prediction model based on the improved variational modal decomposition (VMD) with Informer is proposed in this paper. Firstly
the original meteorological factors such as wind speed
wind direction
and pressure are filtered using the random forest model. Secondly
the wind power signal is decomposed by the pelican optimization algorithm-improved VMD algorithm to enhance the accuracy of wind power sequence prediction. Thirdly
multi-step wind power prediction is performed using the Informer model. Finally
the superiority of this model in multi-step wind power prediction is verified through multi-dimensional comparison with other models. The case results demonstrate that the wind power multi-step prediction model based on the improved VMD with Informer exhibits excellent prediction performance and can provide reference for wind power prediction.
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references
WANG Weisheng, WANG Zheng, DONG Cun, et al . Status and error analysis of short-term forecasting technology of wind power in China[J ] . Automation of Electric Power Systems, 2021, 45(1): 17−27(in Chinese).
CUI Mingyong, DONG Wentao, LU Zhigang. CEEMDAN-CNN-LSTM short-term wind power prediction based on density clustering[J]. Modern Electric Power, 2024, 41(4): 631−641(in Chinese).
LIU Weipeng, LI Changgang, LIU Yutian, et al . Predictive control of wind turbine for load reduction during ramping events[J ] . International Journal of Electrical Power Energy Systems, 2017, 93(12): 135−145.
QIAO Yan, HAN Li, LI Mengjie. Interval estimation of wind power forecasting error based on ramp features and cloud model[J]. Automation of Electric Power Systems, 2022, 46(11): 75−84(in Chinese).
LI Jinghua, SANG Chuanchuan, GAN Yifu, et al . A review of researches on wind power forecasting technology[J ] . Modern Electric Power, 2017, 34(3): 1−11(in Chinese).
ENVELOP A, ENVELOPE A E. Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks[J]. Renewable Energy, 2022, 201(1): 1076-1085.
WANG Quan, WANG Fei, LI Zhongheng, et al . Efficient random subspace decision forests with a simple probability dimensionality setting scheme[J ] . Information Sciences, 2023, 638: 118993.
FOLEY A M, LEAHY P G, MARVUGLIA A, et al . Current methods and advances in forecasting of wind power generation[J ] . Renewable Energy, 2012, 37(1): 1−8.
KUSIAK A, ZHANG Z, VERMA A. Prediction, operations, and condition monitoring in wind energy[J]. Energy, 2013, 60(7): 1−12.
WANG Yun, LI Yifen, ZOU Runmin, et al . Sparse heteroscedastic multiple spline regression models for wind turbine power curve modeling[J ] . IEEE Transactions on Sustainable Energy, 2020, 12(1): 191−201.
WANG Huaizhi, LI Gangqiang, WANG Guibin, et al . Deep learning based ensemble approach for probabilistic wind power forecasting[J ] . Applied Energy, 2017, 188(9): 56−70.
HU Shuai, XIANG Yue, SHEN Xiaodong, et al . Wind power prediction model considering meteorologic al factor and spatial correlation of wind speed[J ] . Automation of Electric Power Systems, 2021, 45(7): 28−36(in Chinese).
WU Qianyu, GUAN Fei, LV Chen, et al . Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM[J ] . IET Renewable Power Generation, 2021, 15(5): 1019−1029.
YE Ruili, GUO Zhizhong, LIU Ruiye, et al . wind speed and wind power forecasting method based on wavelet packet decomposition and improved elman neural network[J ] . Transactions of China Electrotechnical Society, 2017, 32(21): 103−111.
LU Peng, YE Lin, ZHAO Yongning, et al . Review of meta-heuristic algorithms for wind power prediction. methodologies, applications and challenges[J ] . Applied Energy, 2021, 301(C): 117446.
LIU Xuli, MO Yuchang, WU Zhe, et al . Ultra short term photovoltaic power prediction based on DWT-CNN-LSTM[J ] . Journal of Zhengzhou University (Science Edition), 2022, 54(4): 86−94(in Chinese).
HAQUE A U, NEHRIR M H, MANDAL P. A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting[J]. IEEE Transactions on Power Systems, 2014, 29(4): 1663−1672.
GAO Xiaozhi, GUO Wang, MEI Chunxiao, et al . Short-term wind power forecasting based on SSA-VMD-LSTM[J ] . Energy Reports, 2023, 9(10): 335−344.
ZHANG Yagang, PAN Guifang, CHEN Bing, et al . Short-term wind speed prediction model based on GA-ANN improved by VMD[J ] . Renewable Energy, 2020, 156: 1373−1388.
LI Jiale, SONG Zihao, WANG Xuefei, et al . A novel offshore wind farm typhoon wind speed prediction model based on PSO-Bi-LSTM improved by VMD[J ] . Energy, 2022, 251: 123848.
ZHU Xishan, LUO Zhen, YI Cancan, et al . Prediction method of elevator wire rope slippage based on POA-CNN-REGST model[J ] . Journal of Mechanical Electrical Engineering, 2023, 40(6): 928−935(in Chinese).
SUN Shilin, LIU Yuekai, LI Qi, et al . Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks[J ] . Energy Conversion and Management, 2023, 283: 116916.
LIN Yong, ZHANG Haiing, LIU Jiyan, et al . Research on short-term wind power Prediction of GRU based on similar days[J ] . Journal of Physics: Conference Series, 2021, 2087(1): 012089.
SHI Zhuojian, RAN Qiwu, XU Fucong. Short-term load forecasting based on aggregated secondary decomposition and informer[J]. Power System Technology, 2024, 48(6): 2574−2583(in Chinese).
GONG Mingju, YAN Changcheng, XU Wei, et al . Short-term wind power forecasting model based on temporal convolutional network and informer[J ] . Energy, 2023, 283: 129171.
HAN Li, YU Xiaojiao, YU Hongbo, et al . Wind power interval prediction based on fluctuation trend segmentation[J ] . Automation of Electric Power Systems, 2023, 47(18): 206−215(in Chinese).
PANG Chuanjun, SHANG Xuewei, ZHANG Bo, et al . Short-term wind power probability prediction based on improved gradient boosting machine algorithm[J ] . Automation of Electric Power Systems, 2022, 46(16): 198−206(in Chinese).
YU Jianming, PANG Chuanjun. Short-term wind power probabilistic prediction considering data and model uncertainties[J]. Power System Technology, 2022, 46(5): 1926−1933(in Chinese).
YANG Mao, DONG Hao. Short-term wind power interval prediction based on wind speed of numerical weather prediction and Monte Carlo method[J]. Automation of Electric Power Systems, 2021, 45(5): 79−85(in Chinese).
FANG Yuan, LU Xiaoqing, LI Hongyang. A random forest-based model for the prediction of construction-stage carbon emissions at the early design stage[J]. Journal of Cleaner Production, 2021, 328: 129657.
TROJOVSKÝ P, DEHGHANI M. Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications[J]. Sensors, 2022, 22(3): 855−861.
LI Junqing, GENG Jiya, GUO Xiaoyu, et al . A method for wind turbine bearing deterioration index construction and fault diagnosis by combining POA-VMD and autoencoder[J ] . Machine Tool Hydraulics, 2024, 52(13): 219−226(in Chinese).