Abstract: A method includes obtaining data associated with operation of a monitored system. The method also includes using one or more first machine learning models to identify anomalies in the monitored system based on the obtained data, where each anomaly identifies an anomalous behavior. The method further includes using one or more second machine learning models to classify each of at least some of the identified anomalies into one of multiple classifications. Different ones of the classifications are associated with different types of cyberthreats to the monitored system, and the identified anomalies are classified based on risk scores determined using the one or more second machine learning models. In addition, the method includes identifying, for each of at least some of the anomalies, one or more actions to be performed in order to counteract the cyberthreat associated with the anomaly.

Abstract: A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

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 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 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.