Bayesian-Optimized Bidirectional Long-Short-Term Memory network for Wind Power Forecasting with Uncertainty Quantification
Citation
Abstract
Objective
Wind energy technologies, including advanced management and scheduling, rely on accurate wind power forecasting (WPF) for optimal operation. Enhancing forecast precision is crucial for reducing volatility in wind power and improving forecasting reliability. While forecasting methods estimate future values from historical data, traditional approaches often struggle with computational efficiency and model complexity. To address these challenges, we propose a hybrid forecasting model that integrates multi- variate estimation (MVE) and pure prediction (TSP) using a bidirectional long-short-term memory network (Bi- LSTM) optimized with Tree-structured Parzen Estimator (TPE)- based Bayesian optimization. The model incorporates numerical weather prediction (NWP) data for real-time forecasting, a key limitation in existing methods. MVE utilizes features such as wind speed, direction, temperature, and pressure, while TSP captures historical power generation patterns. The TPE-optimized Bi-LSTM architecture effectively captures bidirectional temporal dependencies, improving in both short-term and long-term forecasting. The model is evaluated using a six-year historical wind energy dataset from NREL, with performance assessed through RMSE, MAE, and R2 score. It outperforms traditional LSTM variants (Vanilla LSTM, Stacked LSTM, Bi-LSTM) and state-of-the-art models such as Transformers and GRUs, achieving R² scores of 0.976 for MVE and 0.932, 0.928, and 0.864 for TSP across short-term, day- ahead, and long-term forecasting, respectively. Additionally, TPE based Bayesian optimization reduces computational time around 8-10%, enhancing hyperparameter tuning efficiency. The study further analyzes the model’s computational burden, scalability, and practical implementation, offering a robust and efficient approach for improving wind power forecasting accuracy.
Method
(\(N_{ASD}=23\), \(N_{NT}=52\))