上海交通大学学报

上海交通大学学报

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基于闭环经验模态分解和时序卷积特征提取的风光短期功率预测方法

  

  1. 1. 上海电力大学 电气工程学院，上海 200090；

    2. 上海电力大学 自动化工程学院，上海200090；

    3. 上海发电过程智能管控工程技术研究中心，上海 200090

  • 作者简介:王丹豪(1992—)，博士生，从事综合智慧能源、虚拟电厂等领域研究
  • 基金资助:
    国家自然科学基金资助项目(62373241)，上海市科学技术委员会资助项目(20020500500)

Short-Term Wind and Solar Power Forecasting Method Based on Closed-Loop Empirical Mode Decomposition and Temporal Convolutional Feature Extraction

  1. 1. College of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 

    2. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 

    3. Shanghai Engineering Research Center of Intelligent Management and Control for Power Process, Shanghai 200090, China

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摘要： 针对风光发电具有强烈的随机性和波动性，使得单一预测模型在处理其非线性和非平稳特性时呈现一定不足等问题，提出了一种基于闭环完全集成经验模态分解（CEEMDAN）和时序卷积特征提取的风光短期功率组合预测模型。首先，采用CEEMDAN对原始风光发电功率数据进行多时间尺度分解，从中提取高能量的本征模态函数，有效减少数据的非平稳性和复杂性。然后，通过时序卷积网络（TCN）提取风光数据的时序特征，双向门控循环单元（BiGRU）进一步捕获时间序列的双向动态特性，并通过注意力（Attention）机制增强对关键特征的关注，构建了TCN-BiGRU-Attention预测模型。此外，引入尊海鞘群算法（SSA）对模型超参数进行优化，并通过反馈机制动态调整CEEMDAN的分解参数，进一步改善预测效果。最后，通过某区域的风光发电数据进行实验，结果表明，所提出的模型相较于其它对比预测模型，其R2分别提高了2.26%和2.79%，有效提升了预测精度。

关键词: 功率预测, 闭环模态分解, 樽海鞘群优化算法, 时序卷积特征提取, 深度学习

Abstract: In view of the strong randomness and volatility inherent in wind and solar power generation—which render single forecasting models somewhat inadequate for handling their nonlinear and non-stationary characteristics—this paper proposes a combined short-term forecasting model for wind and solar power based on closed-loop complete ensemble empirical mode decomposition (CEEMDAN) and temporal convolution feature extraction. Firstly, CEEMDAN is applied to decompose the original wind and solar power generation data across multiple time scales, extracting high-energy intrinsic mode functions (IMFs) to effectively reduce the data’s non-stationarity and complexity. Then, a Temporal Convolutional Network (TCN) is employed to extract the temporal features of the wind and solar data, while a Bidirectional Gated Recurrent Unit (BiGRU) further captures the bidirectional dynamic characteristics of the time series; an Attention mechanism is also integrated to enhance the focus on key features, thereby constructing the TCN-BiGRU-Attention forecasting model. In addition, the Salp Swarm Algorithm (SSA) is introduced to optimize the model’s hyperparameters, and a feedback mechanism is used to dynamically adjust the decomposition parameters of CEEMDAN, further improving the forecasting performance. Finally, experiments conducted using wind and solar power generation data from a certain region demonstrate that, compared with other forecasting models, the proposed model achieves increases in R² of 2.26% and 2.79%, respectively, effectively enhancing the prediction accuracy.

Key words: power forecasting, closed-loop mode decomposition, salp swarm algorithm (SSA), temporal convolutional feature extraction, deep learning

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