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1. Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

   https://doi.org/10.1115/POWER2020-16557  

   Choe, Do-Eun; Talor, Gary; Kim, Changkyu (January 2020, ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering)

   null (Ed.)

   Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size. 

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2. Bayesian-optimized bidirectional long-short-term memory network for wind power forecasting with uncertainty quantification

   https://doi.org/10.1016/j.epsr.2025.112261  

   Hussain, Arif; Kumar, Kundan; Ravikumar, Gelli (February 2026, Electric Power Systems Research)

   Wind energy technologies, including advanced turbine management and scheduling, rely on accurate wind power forecasting (WPF) for optimal operation. Enhancing forecast precision is crucial for reducing volatility in wind power integration and improving forecasting reliability. While time series 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 time series 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, addressing a key limitation in existing methods. MVE utilizes meteorological 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 accuracy 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. While the model achieves high accuracy on the NREL dataset, future work will validate its performance across diverse geographic regions (e.g., Europe, Asia) and turbine configurations. 

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3. Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model

   https://doi.org/10.3390/a18040206  

   Barbre, Zachary; Li, Gang (April 2025, Algorithms)

   This paper presents an innovative approach to wind energy forecasting through the implementation of an extended long short-term memory (xLSTM) model. This research addresses fundamental limitations in time-sequence forecasting for wind energy by introducing architectural enhancements to traditional LSTM networks. The xLSTM model incorporates two key innovations: exponential gating with memory mixing and a novel matrix memory structure. These improvements are realized through two variants, i.e., scalar LSTM and matrix LSTM, which are integrated into residual blocks to form comprehensive architectures. The xLSTM model was validated using SCADA data from wind turbines, with rigorous preprocessing to remove anomalous measurements. Performance evaluation across different wind speed regimes demonstrated robust predictive capabilities, with the xLSTM model achieving an overall coefficient of determination value of 0.923 and a mean absolute percentage error of 8.47%. Seasonal analysis revealed consistent prediction accuracy across varied meteorological patterns. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling. These results demonstrate the potential of xLSTM for improving wind power forecasting accuracy, which is crucial for optimizing turbine operations and grid integration of renewable energy resources. 

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4. Time-Series AI Forecasting of Wind-to-Hydrogen Production

   https://doi.org/10.1115/ES2025-156886  

   Bounaim, Doha; Li, Gang (July 2025, American Society of Mechanical Engineers)

   Abstract The transition to carbon-neutral energy has increased the reliance upon renewable sources of energy, e.g., wind power, placing added demands on resilience and stability of the power grid. Wind-to-hydrogen production systems can be a solution for addressing these demands. By converting excess wind energy into hydrogen via electrolysis, these systems can effectively store the intermittent energy generated by wind turbines. This study discusses the application of two time-series prediction models, i.e., long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), in forecasting the energy of wind farms, subsequently used for an assessment of hydrogen production means of a proton exchange membrane (PEM) electrolyzer. Using a dataset comprising wind speed, active power, and wind direction, inputs were normalized, and wind direction was transformed into sine and cosine components to retain circular characteristics. Bi-LSTM demonstrated superior accuracy with lower testing RMSE than LSTM. Integrating wind forecasts with a PEM electrolyzer model, incorporating critical electro-chemical parameters, revealed an optimal efficiency of 63.611% at a membrane thickness of 0.00254 cm and a temperature of 70°C. Bi-LSTM forecasts boosted hydrogen production by 5% to 8% compared to LSTM. 

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5. An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines

   https://doi.org/10.3390/s24248167  

   Dai, Jiazhi; Rotea, Mario; Kehtarnavaz, Nasser (December 2024, Sensors)

   Freezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GRU) for the purpose of high-accuracy and long-term prediction of icing on wind turbine blades. This model combines three networks, the CNN, TCN, and GRU, in order to incorporate both the temporal aspect of SCADA time-series data as well as the dependencies of SCADA variables. The experimentations conducted by using this model and SCADA data from three wind turbines in a wind farm have generated average prediction accuracies of about 97% for prediction horizons of up to 2 days ahead. The developed model is shown to maintain at least 95% prediction accuracy for long prediction horizons of up to 22 days ahead. Furthermore, for another wind farm SCADA dataset, it is shown that the developed PCTG model achieves over 99% icing prediction accuracy 10 days ahead. 

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