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

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub 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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引用次数: 0

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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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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