The top-ranked assignee in this dataset holds 26 of 386 records in scope, with the field dropping off gradually after that — fifth place holds 13 and tenth holds 9. Combined, the top five assignees account for 25.6% of all records and the top ten account for 39.9%, meaning most of the field's filings sit outside the ranked leaders. This pattern is typical of a technology area with an established core of turbine OEMs and grid suppliers alongside a long tail of research institutions and single-filing entrants.

Filings grew sharply from 8 in 2021 to 21 in 2024, a +163% increase over three years, which is the clearest and most reliable growth signal in this dataset. Figures for 2025 and 2026 appear lower or partial, but that reflects publication lag of roughly 18 months rather than an actual slowdown — recent filings simply have not all published yet. Any read on the very latest years should treat the counts as a floor rather than a final figure.

Turbine hardware (IPC class F03D) is the single largest category at 36.0% of the 386 records, but digital data processing (G06F) is close behind at 29.0%, followed by power/grid systems (H02J) at 22.0% and meteorology (G01W) at 15.3%. Because a single patent can carry several IPC classes, these shares add up to more than 100% of the record total — the overlap itself shows how much of the field spans turbine control, grid integration and computational forecasting simultaneously. AI-based computing (G06N) appears in a smaller but notable 7.8% of records, suggesting machine-learning approaches are an emerging rather than dominant thread.

The technology composition data suggests the overlap between meteorology (G01W, 15.3%), velocity/acceleration sensing (G01P, 10.1%) and AI-based computing (G06N, 7.8%) is comparatively thin next to the dominant F03D and G06F classes, which points to short-term ramp forecasting and physically-grounded boundary-layer modelling as areas with room to file. The long tail below the top ten ranked assignees, who together hold only 39.9% of records, also indicates that no single company has locked down adjacent branches like rotor-torque response to forecast conditions. Any white-space assessment should be paired with a full claims review, since IPC class presence indicates activity, not necessarily claim breadth.

US10879702B2, assigned to the Trustees of Princeton University and published in December 2020, covers a short-term wind forecasting method anchored in a physical model of atmospheric boundary-layer dynamics rather than a purely statistical or machine-learning approach. Anyone building a forecasting system that models unsteady atmospheric dynamics to predict wind conditions one to five hours ahead using prior observations at the same location should review this filing's claims closely. It does not appear among the most-cited records in this dataset, so its influence should be weighed against the more heavily cited commercial-value-optimisation family rather than treated as the field's dominant reference.