ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON INFORMATION ENTROPY CLUSTERING DECOMPOSITION AND CTA-BILSTM

Li Tianbai; Gu Junhua; Qin Yulong; Zhang Suqi

doi:10.19912/j.0254-0096.tynxb.2024-1607

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 604-612. DOI: 10.19912/j.0254-0096.tynxb.2024-1607

ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON INFORMATION ENTROPY CLUSTERING DECOMPOSITION AND CTA-BILSTM

Li Tianbai¹, Gu Junhua^1,2, Qin Yulong¹, Zhang Suqi³

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Abstract

To address the non-stationarity and volatility of wind power series, this paper proposes a short-term wind power forecasting framework consisting of two parts： information entropy clustering decomposition and a channel-time attention bidirectional long short-term memory network forecasting model. Firstly, the wind power series undergoes information entropy clustering decomposition. The improved complete ensemble empirical mode decomposition with adaptive noise is utilized for the initial decomposition, and the high-complexity components derived from this process are subsequently decomposed using variational mode decomposition. Based on information entropy, components with high similarity are clustered to form new modal components.Secondly, these decomposed components are fed into the CTA-BiLSTM forecasting model. This model employs both channel attention and temporal attention mechanisms to assign different weights to features based on their importance. Finally, experiments are conducted using a dataset from a wind farm in northwest China. The experimental results show that the proposed framework attains superior forecasting accuracy in comparison to current state-of-the-art models.

Key words

wind power / forecasting / mode decomposition / information entropy / bidirectional long short-term memory network / channel attention / temporal pattern attention

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Li Tianbai, Gu Junhua, Qin Yulong, Zhang Suqi. ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON INFORMATION ENTROPY CLUSTERING DECOMPOSITION AND CTA-BILSTM[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 604-612 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1607

References

[1] 陈超, 王金忠, 吴焰龙. 新能源电力系统的规划及应用研究[J]. 能源与环保, 2022, 44(8): 217-221, 228.CHEN C, WANG J Z, WU Y L. Research on planning and application of new energy power system[J]. China energy and environmental protection, 2022, 44(8): 217-221, 228.
[2] WANG Y, ZOU R M, LIU F, et al.A review of wind speed and wind power forecasting with deep neural networks[J]. Applied energy, 2021, 304: 117766.
[3] TAWN R, BROWELL J.A review of very short-term wind and solar power forecasting[J]. Renewable and sustainable energy reviews, 2022, 153: 111758.
[4] HOSSAIN M A, GRAY E, LU J W, et al.Optimized forecasting model to improve the accuracy of very short-term wind power prediction[J]. IEEE transactions on industrial informatics, 2023, 19(10): 10145-10159.
[5] 时志雄, 朱峰, 刘舒, 等. 基于1DCNN-DACLSTM模型的风电超短期功率预测方法[J]. 电子器件, 2024, 47(1): 194-200.SHI Z X, ZHU F, LIU S, et al. Ultra-short-term forecasting of wind power based on 1DCNN-DACLSTM model[J]. Chinese journal of electron devices, 2024, 47(1): 194-200.
[6] ABOU HOURAN M, SALMAN BUKHARI S M, ZAFAR M H, et al. COA-CNN-LSTM: coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications[J]. Applied energy, 2023, 349: 121638.
[7] 郑真, 朱峰, 马小丽, 等. 基于TL-LSTM的新能源功率短期预测[J]. 综合智慧能源, 2023, 45(1): 41-48.ZHENG Z, ZHU F, MA X L, et al. Short-term new energy power prediction based on TL-LSTM[J]. Integrated intelligent energy, 2023, 45(1): 41-48.
[8] 吕云龙, 胡琴, 熊俊杰, 等. 基于时空注意力卷积模型的超短期风电功率预测[J]. 电网技术, 2024, 48(5): 2064-2073.LYU Y L, HU Q, XIONG J J, et al. Ultra-short-term wind power prediction based on spatiotemporal attention convolution model[J]. Power system technology, 2024, 48(5): 2064-2073.
[9] WAN A P, CHANG Q, AL-BUKHAITI K, et al.Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism[J]. Energy, 2023, 282: 128274.
[10] 唐杰, 李彬. 基于EMD-PSO-BiLSTM组合模型的短期风电功率预测[J]. 自动化应用, 2024(5): 126-129.TANG J, LI B. Short-term wind power prediction based on EMD-PSO-BiLSTM combined model[J]. Automation application, 2024(5): 126-129.
[11] 杨国华, 祁鑫, 贾睿, 等. 基于CEEMD-SE的CNN&LSTM-GRU短期风电功率预测[J]. 中国电力, 2024, 57(2): 55-61.YANG G H, QI X, JIA R, et al. Short-term wind power forecast based on CNN & LSTM-GRU model integrated with CEEMD-SE algorithm[J]. Electric power, 2024, 57(2): 55-61.
[12] ZHANG C, PENG T, NAZIR M S.A novel integrated photovoltaic power forecasting model based on variational mode decomposition and CNN-BiGRU considering meteorological variables[J]. Electric power systems research, 2022, 213: 108796.
[13] ZHAO Z N, YUN S N, JIA L Y, et al.Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features[J]. Engineering applications of artificial intelligence, 2023, 121: 105982.
[14] 傅质馨, 王宝池, 刘皓明, 等. 基于风速-功率组合分解重构的海上风电功率预测[J]. 太阳能学报, 2024, 45(11): 418-426.FU Z X, WANG B C, LIU H M, et al. Offshore wind power forecasting based on wind speed-power combination decompositon[J]. Acta energiae solaris sinica, 2024, 45(11): 418-426.
[15] 姜建国, 杨效岩, 毕洪波. 基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测[J]. 太阳能学报, 2024, 45(7): 462-473.JIANG J G, YANG X Y, BI H B. Photovoltaic power forecasting method based on VMD-FE-CNN-BiLSTM[J]. Acta energiae solaris sinica, 2024, 45(7): 462-473.
[16] 张广伦, 钟海旺. 信息熵在电力系统中的应用综述及展望[J]. 中国电机工程学报, 2023, 43(16): 6155-6181.ZHANG G L, ZHONG H W. Review and prospect of information entropy and its applications in power systems[J]. Proceedings of the CSEE, 2023, 43(16): 6155-6181.
[17] LI K, MU Y C, YANG F, et al.Joint forecasting of source-load-price for integrated energy system based on multi-task learning and hybrid attention mechanism[J]. Applied energy, 2024, 360: 122821.
[18] SHIH S Y, SUN F K, LEE H Y.Temporal pattern attention for multivariate time series forecasting[J]. Machine learning, 2019, 108(8): 1421-1441.
[19] 李浩, 赵青, 崔辰州, 等. 基于CNN与LSTM复合深度模型的恒星光谱分类算法[J]. 光谱学与光谱分析, 2024, 44(6): 1668-1675.LI H, ZHAO Q, CUI C Z, et al. A stellar spectrum classification algorithm based on CNN and LSTM composite deep learning model[J]. Spectroscopy and spectral analysis, 2024, 44(6): 1668-1675.
[20] 李争, 张杰, 徐若思, 等. 基于相似日聚类和PCC-VMD-SSA-KELM模型的短期光伏功率预测[J]. 太阳能学报, 2024, 45(2): 460-468.LI Z, ZHANG J, XU R S, et al. Short term photovoltaic power prediction based on similar day clustering and PCC-VMD-SSA-KELM model[J]. Acta energiae solaris sinica, 2024, 45(2): 460-468.
[21] 刘国海, 孙文卿, 吴振飞, 等. 基于Attention-GRU的短期光伏发电功率预测[J]. 太阳能学报, 2022, 43(2): 226-232.LIU G H, SUN W Q, WU Z F, et al. Short-term photovoltaic power forecasting based on Attention-GRU model[J]. Acta energiae solaris sinica, 2022, 43(2): 226-232.