General Electric Company

Krishna Kumar Swaminathan

Rahul Srivastava

Pritesh Jain

Sridhar Dasaratha

Nitika Bhaskar

Milesh Shrichandra Gogad

Deepak Raj Sagi

Rahul Kumar Srivastava

Shishir Goel

The present discussion relates to generating power generation forecasts both on-site and remote to a wind farm, or other intermittent power generation asset, so as to increase the reliability of providing a forecast to interested parties, such as regulatory authorities. Forecasts may be separately generated at both the on-site and remote locations and, if both are available, one is selected for transmission to interested parties, such as regulatory authorities. If, due to circumstances, one forecast is unavailable, the other forecast may be used in its place locally and remotely, communications permitting.

Method and system to optimize availability, transmission, and accuracy of wind power forecasts and schedules

A wind power generation system includes one or both of a memory or storage device storing one or more processor-executable executable routines, and one or more processors configured to execute the one or more executable routines which, when executed, cause acts to be performed. The acts include receiving weather data, wind turbine system data, or a combination thereof; transforming the weather data, the wind turbine system data, or the combination thereof, into a data subset, wherein the data subset comprises a first time period data; selecting one or more wind power system models from a plurality of models; transforming the one or more wind power system models into one or more trained models at least partially based on the data subset; and executing the one or more trained models to derive a forecast, wherein the forecast comprises a predicted electrical power production for the wind power system.

Systems and methods for improved wind power generation

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Bengaluru, India

Krishna Kumar Swaminathan