Reducing curtailment of wind power generation

摘要

Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.