Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint

Research output: Contribution to conferencePaper


In an autonomous wind farm, turbines will use information from nearby turbines to achieve wind farm-level objectives such as optimizing the overall performance of a wind farm, ensuring resiliency when other sensors fail, and adapting to changing local conditions. In this paper, the wind farm can be modeled as a network within which turbines (nodes) share information across designated communication channels, with a focus on turbines at the outside of the wind farm capturing local effects and sharing that information with downstream turbines. Understanding of varied inflow conditions can be especially important in complex terrain. This information can be used to monitor turbines, self-organize turbines into groups, and predict the power performance of a wind farm. In particular, this paper describes an autonomous wind farm that incorporates information from local sensors in real time to predict wind speed and wind direction at each turbine over a short-term horizon. Results indicate that the estimate of wind direction can be used to improve the knowledge of the wind speed and direction over the persistence method on a 10- 15-minute time horizon. These short-term forecasts can also be used to facilitate advanced control methods such as feedforward control within a wind farm.
Original languageAmerican English
Number of pages9
StatePublished - 2019
EventIEEE American Control Conference - Philadelphia, Pennsylvania
Duration: 10 Jul 201912 Jul 2019


ConferenceIEEE American Control Conference
CityPhiladelphia, Pennsylvania

Bibliographical note

See NREL/CP-5000-75050 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5000-73396


  • distributed optimization
  • performance
  • short-term forecasting
  • wind energy
  • wind farm control


Dive into the research topics of 'Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint'. Together they form a unique fingerprint.

Cite this