  Wind power prediction, especially for turbines, is vital for the operation,
controllability, and economy of electricity companies. Hybrid methodologies
combining advanced data science with weather forecasting have been
incrementally applied to the predictions. Nevertheless, individually modeling
massive turbines from scratch and downscaling weather forecasts to turbine size
are neither easy nor economical. Aiming at it, this paper proposes a novel
framework with mathematical underpinnings for turbine power prediction. This
framework is the first time to incorporate knowledge distillation into energy
forecasting, enabling accurate and economical constructions of turbine models
by learning knowledge from the well-established park model. Besides, park-scale
weather forecasts non-explicitly are mapped to turbines by transfer learning of
predicted power errors, achieving model correction for better performance. The
proposed framework is deployed on five turbines featuring various terrains in
an Arctic wind park, the results are evaluated against the competitors of
ablation investigation. The major findings reveal that the proposed framework,
developed on favorable knowledge distillation and transfer learning parameters
tuning, yields performance boosts from 3.3 % to 23.9 % over its competitors.
This advantage also exists in terms of wind energy physics and computing
efficiency, which are verified by the prediction quality rate and calculation
time.
