Deep Learning-Based Wind Power Forecasting and Data Center Load Response Optimization Under Extreme Weather Events

Abstract

The increasing penetration of wind energy and the rapid growth of computing-intensive data centers have made the joint management of renewable-generation uncertainty and flexible electricity demand a pressing concern, particularly during extreme weather events such as storms, cold snaps, and rapid wind ramps. This paper proposes an integrated framework that couples a deep learning-based wind power forecasting model with a data center load response optimization scheme. A convolutional neural network-bidirectional long short-term memory network with an attention mechanism (CNN-BiLSTM-Attention) is developed to capture both local ramp features and long-range temporal dependencies in wind power time series, and an auxiliary extreme-weather risk classifier flags high-uncertainty forecasting windows. The resulting probabilistic forecasts feed a mixed-integer linear programming (MILP) optimizer that reschedules deferrable data center workloads, migrates jobs across sites, and adjusts cooling set-points to align computing load with available wind generation. Simulation results using SCADA wind-farm data and a representative multi-site data center workload show that the proposed forecasting model reduces RMSE by up to 20.7% relative to Transformer and CNN-LSTM baselines under extreme-weather subsets, while the load-response strategy achieves up to 18.7% energy-cost savings with service-level-agreement (SLA) completion above 94% during severe storm scenarios.