Lee Sangkeum, Almomani Mohammad H, Alomari Saleh Ali, Saleem Kashif, Smerat Aseel, Snasel Vaclav, Gandomi Amir H, Abualigah Laith
Department of Computer Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon, Republic of Korea.
Department of Mathematics, Facility of Science, The Hashemite University, P.O box 330127, 13133, Zarqa, Jordan.
Sci Rep. 2025 May 28;15(1):18746. doi: 10.1038/s41598-025-97793-8.
Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets-NREL WIND, EMD WIND, WWSIS, and ERCOT GRID-and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil's Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications.
准确的超短期风电功率预测对于可再生能源可靠接入现代电力系统至关重要。然而，风电功率数据固有的变异性和非线性带来了重大挑战。为解决这些问题，本研究提出了一种新颖的混合深度学习框架IAPO-LSTM，它结合了用于空间特征提取的卷积神经网络（CNN）和用于时间序列建模的门控循环单元（GRU）。该模型使用增强型人工原生动物优化器（IAPO）进行优化，IAPO通过自适应环境响应机制（AERM）进行增强，该机制根据问题态势动态调整探索和利用策略，以提高收敛性和超参数调整效率。所提出的IAPO-LSTM模型在四个真实世界数据集——NREL WIND、EMD WIND、WWSIS和ERCOT GRID上进行了评估，并与六个最先进的预测模型进行了基准测试。结果表明，IAPO-LSTM在所有数据集中实现了最低的预测误差，在ERCOT数据集上的平均绝对误差（MAE）低至2.78，均方根误差（RMSE）为4.50，泰尔不等式系数（TIC）为0.0292。此外，与基线方法相比，该模型展示了更快的推理时间和更好的统计显著性（p < 0.005）。这些结果证实，IAPO-LSTM不仅高度准确，而且对于实时风电功率预测应用高效且稳健。
