Eulerian-DL Forecasting of Wind Turbulence for Wind Turbines: Enabling Smart Sustainable Energy

Currently, wind power is the fastest-growing sus-tainable energy resource, and accurately predicting wind velocity for individual turbines is essential. Wind behavior is turbulent, with variable patterns, characteristics, and unpredictable extreme events. Moreover, the wind reaching each turbine is impacted by wake loss effects from surrounding turbines, which underscores the need for a precise local prediction model for each turbine to enhance accuracy in smart wind power production. This study proposes a novel approach using local measurements at fixed points, adopting the Eulerian perspective in fluid dynamics to forecast wind turbulence for the coming hour using a deep learning model. A long short-term memory (LSTM) forecasting model is tuned and applied. Laboratory data from wind tunnel tests are utilized to account for velocity fluctuations and wake loss effects, aiming to establish similarity to an affected single wind turbine. Model training and inference run on the high-performance computing (HPC) DEEP-DAM systems at the Julich Supercomputing Centre. Three train-test datasets are employed to evaluate the model. The measured metrics indicate that the mean absolute error is 0.048, the root mean square error is approximately 0.065, and the Pinball loss for the 90th percentile is 0.0042. The model demonstrates a robust predictive capability, supporting the development of advanced instruments for individ-ual turbines and facilitating smart, clean, and sustainable energy production.