논문 해외 국제전문학술지(SCI급) A Novel Deep Learning Framework with Artificial Protozoa Optimization-based Adaptive Environmental Response for Wind Power Prediction
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2025-05
- 저자명 Sangkeum Lee, Mohammad H. Almomani, Saleh Ali Alomari, Kashif Saleem, Aseel Smerat, Vaclav Snasel7, Amir H. Gandomi, Laith Abualigah
- 학술지명 Scientific reports
- 발행국가 해외
- 전체저자수 8
- 원문 링크 https://doi.org/10.1038/s41598-025-97793-8
홈페이지논문 초록 (Abstract)
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.