Digital Twin-Based Wind Forecasting and Grid Integration for Clean Energy

Abstract

As the capacity of wind generation is increasing, it is necessary to forecast and control methods that can cope with rapid weather variation but still ensure reliable operation of the grid. This work presents a digital twin-based framework that integrates turbine physics, real-time measurements, adaptive state estimation, short-term power forecasting, and coordinated grid energy management. The virtual model is continuously updated with sampled operating data and mirrors aerodynamic conversion, rotor dynamics, converter operation, direct-current-link behaviour, and active-reactive power exchange. The forecasting horizon is sixty minutes, with five-minute sampling and synchronization intervals for the digital twin. The multi-task forecasting model is trained with a composite objective function that balances prediction accuracy, grid-power tracking and parameter regularization. The modelling results closely relate the measured and forecasted wind power values for varying operating conditions. The digital twin gives an MAE of 0.07 MW and $\mathbf{R}^{\mathbf{2}}$ of 0.98, outperforming autoregressive integrated moving average, support vector regression, artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network-long short-term memory models. It also smoothies grid-side energy management and increases operational consistency. For practical wind variability and uncertainty, the framework enables clean-energy access, renewable integration, efficient asset utilization, and resilient low-carbon power infrastructure without requiring intrusive alterations to the physical turbine system.