Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction

IF 4.1 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES iScience Pub Date : 2025-05-16 Epub Date: 2025-04-06 DOI:10.1016/j.isci.2025.112360

Chunliang Mai , Lixin Zhang , Omar Behar , Xue Hu , Xuewei Chao

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Abstract

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%, p < 0.001), and training time reduced by 12.1%–21.3%. This method offers accurate, scalable forecasting for reliable renewable energy integration.

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风电功率预测的二次误差修正自适应奇异谱分解混合框架

风电功率的高精度预测是电网调度和可再生能源利用的必要条件。风数据的非线性、随机和多尺度特征给预测带来了挑战。本研究提出了一种融合自适应改进奇异谱分析（ISSA）、优化双向时间卷积网络、双向长短期记忆（bitn - bilstm）网络和AdaBoost集成学习的混合模型。自适应ISSA提供无参数、数据驱动的模态分解来降低噪声。混合策略增强蜣螂优化（OTDBO）对BiTCN-BiLSTM的超参数进行微调，AdaBoost动态修正误差，显著提高了鲁棒性。利用达坂城风电场（中国）的季节性数据集进行的测试显示，性能有了实质性的改善(平均绝对误差[MAE]降低了45.4%，均方根误差（RMSE）降低了47.6%,p <；0.001)，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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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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