具有二次误差校正的自适应奇异谱分解混合框架用于风力发电预测。
Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction.
作者信息
Mai Chunliang, Zhang Lixin, Behar Omar, Hu Xue, Chao Xuewei
机构信息
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China.
出版信息
iScience. 2025 Apr 6;28(5):112360. doi: 10.1016/j.isci.2025.112360. eCollection 2025 May 16.
High-precision wind power forecasting is essential for grid scheduling and renewable energy utilization. Wind data's nonlinear, stochastic, and multi-scale characteristics create prediction challenges. This study proposes a hybrid model integrating adaptive improved singular spectrum analysis (ISSA), optimized bidirectional temporal convolutional network-bidirectional long short-term memory (BiTCN-BiLSTM) networks, and AdaBoost ensemble learning. Adaptive ISSA provides parameter-free, data-driven modal decomposition to reduce noise. Hybrid strategy-enhanced dung beetle optimization (OTDBO) fine-tunes hyperparameters of BiTCN-BiLSTM, and AdaBoost dynamically corrects errors, significantly improving robustness. Tests using seasonal datasets from Dabancheng wind farm (China) show substantial performance improvement (mean absolute error [MAE] reduced by 45.4%, root-mean-square error (RMSE) by 47.6%, < 0.001), and training time reduced by 12.1%-21.3%. This method offers accurate, scalable forecasting for reliable renewable energy integration.
高精度风电功率预测对于电网调度和可再生能源利用至关重要。风数据的非线性、随机性和多尺度特性带来了预测挑战。本研究提出了一种混合模型，该模型集成了自适应改进奇异谱分析（ISSA）、优化的双向时间卷积网络-双向长短期记忆（BiTCN-BiLSTM）网络和AdaBoost集成学习。自适应ISSA提供无参数、数据驱动的模态分解以降低噪声。混合策略增强的蜣螂优化算法（OTDBO）对BiTCN-BiLSTM的超参数进行微调，而AdaBoost动态校正误差，显著提高了鲁棒性。使用中国达坂城风电场的季节性数据集进行的测试表明，性能有显著提升（平均绝对误差[MAE]降低了45.4%，均方根误差[RMSE]降低了47.6%，<0.001），且训练时间减少了12.1%-21.3%。该方法为可靠的可再生能源整合提供了准确、可扩展的预测。
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