Short-term wind power prediction based on ICEEMDAN decomposition and BiTCN–BiGRU-multi-head self-attention model

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Electrical Engineering Pub Date : 2024-08-20 DOI:10.1007/s00202-024-02638-8
Xu Zhang, Jun Ye, Lintao Gao, Shenbing Ma, Qiman Xie, Hui Huang
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Abstract

In order to address the security threats posed by the volatility and stochasticity of large-scale distributed wind power, this paper proposes an attention-based hybrid deep learning approach for more efficient and accurate wind power sequence prediction. Firstly, the Pearson correlation coefficient (PCC) is used to identify the main meteorological variables as input sequences. Secondly, the intrinsic complete ensemble empirical mode decomposition with adaptive noise is used to decompose the sequence of wind power. Then, the hidden information such as wind speed, wind direction, and wind magnitude are extracted by bidirectional temporal convolutional networks (BiTCN), and the acquired information is inputted into bidirectional gated recurrent units (BiGRU) optimized by a multi-head self-attention mechanism for prediction. Finally, the predicted values of each component are summed to obtain the final prediction results. By comparing with the other 12 models, the results show that the two-scale integrated model of BiTCN and BiGRU can obtain better prediction accuracy. Compared with other benchmark models, the RMSE of this paper's model is reduced by more than 9.4%, indicating that this paper's model can fit the wind power data better and achieve better prediction results.

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基于 ICEEMDAN 分解和 BiTCN-BiGRU-多机头自关注模型的短期风电预测
为了应对大规模分布式风电的波动性和随机性带来的安全威胁,本文提出了一种基于注意力的混合深度学习方法,以实现更高效、更准确的风电序列预测。首先,使用皮尔逊相关系数(PCC)来识别作为输入序列的主要气象变量。其次,使用带有自适应噪声的本征完全集合经验模式分解来分解风力发电序列。然后,通过双向时序卷积网络(BiTCN)提取风速、风向和风力大小等隐藏信息,并将获取的信息输入经多头自注意机制优化的双向门控递归单元(BiGRU)进行预测。最后,将各部分的预测值相加得出最终预测结果。通过与其他 12 个模型的比较,结果表明 BiTCN 和 BiGRU 的双尺度集成模型可以获得更好的预测精度。与其他基准模型相比,本文模型的均方根误差降低了 9.4%以上,表明本文模型能够更好地拟合风电数据,取得更好的预测效果。
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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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