Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Embodiments herein relate to improving a stochastic forecast for uncertain power generations and demands to quantify an effect on an electrical power grid. To improve the stochastic forecast, a method includes fitting marginal distributions to data of the uncertain power generation and demand by power generation and demand nodes of the electrical power grid. The power generation and demand nodes provide corresponding uncertain power generation and demand based on a renewable energy source. The method also includes determining a correlation structure between the power generation and demand nodes by transforming the data from marginal distributions to a second distribution and by fitting a multivariate time series on transformed data. The method also includes simulating multivariate stochastic forecast with an improved correlation structure.

Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period 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.

Abstract: 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.

Abstract: 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.

Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: 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.

Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments herein relate to improving a stochastic forecast for uncertain power generations and demands to quantify an effect on an electrical power grid. To improve the stochastic forecast, a method includes fitting marginal distributions to data of the uncertain power generation and demand by power generation and demand nodes of the electrical power grid. The power generation and demand nodes provide corresponding uncertain power generation and demand based on a renewable energy source. The method also includes determining a correlation structure between the power generation and demand nodes by transforming the data from marginal distributions to a second distribution and by fitting a multivariate time series on transformed data. The method also includes simulating multivariate stochastic forecast with an improved correlation structure.