Hybrid AI Improves Wind Power Ramp-Event Forecasting

Forecasting sudden, large swings in wind turbine output, known as ramp events, is a significant challenge for energy grids. Standard models often fail to accurately predict these critical occurrences. This research introduces a novel hybrid forecasting approach designed to enhance the prediction of such events. The core innovation involves augmenting traditional forecasting models with "semantic context." Instead of directly applying large language models to raw turbine data, the pipeline converts turbine operating data into simplified text. This text is then transformed into dense embeddings, which serve as additional inputs for ensemble models alongside other features. Evaluations across various time horizons (10, 30, and 60 minutes) using the SDWPF dataset, and external validation on Kaggle SCADA and NREL data, demonstrate that these semantic-context features yield statistically significant, albeit sometimes small, gains over autoregressive, LSTM, and GRU baselines. These improvements are most evident at the 30- and 60-minute horizons, indicating a more robust prediction capability for these challenging events.