Abstract: In some examples, a method of vector-raster data fusion includes receiving vector data for a geographical location, and statistically analyzing the vector data to obtain vector statistics. In some examples the method further includes rasterizing the vector statistics, and storing at least one of the vector data and the rasterized vector statistics together in a key-value store together with previously stored raster data for the geographical location. In some examples, the vector data further includes metadata, and the method further includes storing the metadata in at least one of the key-value store or a separate vector database.

Abstract: Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Abstract: In some examples, a method of vector-raster data fusion includes receiving vector data for a geographical location, and statistically analyzing the vector data to obtain vector statistics. In some examples the method further includes rasterizing the vector statistics, and storing at least one of the vector data and the rasterized vector statistics together in a key-value store together with previously stored raster data for the geographical location. In some examples, the vector data further includes metadata, and the method further includes storing the metadata in at least one of the key-value store or a separate vector database.

Abstract: Embodiments are directed to a computer-implemented method, computer system, and computer program product for forecasting power generation. The method includes receiving a first power generation forecast. A second power generation forecast is received, along with actual observed power generation. A model is created for the observed power generation based on the second power generation forecast. A third power generation forecast is created using the model for the observed power generation, the second power generation forecast, and the first power generation forecast.

Abstract: A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: Embodiments are directed to a computer-implemented method of forecasting power generation. The method includes analyzing, using a processor, a set of historical weather data. A set of historical power generation data is analyzed using the processor. The processor then determines a plurality of clusters, where each cluster of the plurality of clusters represents weather data for a time period and power generated during the time period. The processor then receives a weather forecast for a future time period. The processor determines which cluster of the set of cluster contains weather data that is most similar to the weather forecast. The processor then uses the historical power generation data of the cluster to forecast power generation for the future time period.

Abstract: A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.

Abstract: Embodiments are directed to a computer-implemented method, computer system, and computer program product for forecasting power generation. The method includes receiving a first power generation forecast. A second power generation forecast is received, along with actual observed power generation. A model is created for the observed power generation based on the second power generation forecast. A third power generation forecast is created using the model for the observed power generation, the second power generation forecast, and the first power generation forecast.

Abstract: Embodiments are directed to a computer-implemented method of forecasting power generation. The method includes analyzing, using a processor, a set of historical weather data. A set of historical power generation data is analyzed using the processor. The processor then determines a plurality of clusters, where each cluster of the plurality of clusters represents weather data for a time period and power generated during the time period. The processor then receives a weather forecast for a future time period. The processor determines which cluster of the set of cluster contains weather data that is most similar to the weather forecast. The processor then uses the historical power generation data of the cluster to forecast power generation for the future time period.

Abstract: Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Abstract: A set of characteristics of a period is extracted from a forecast that is applicable to the period and a geographical area. From a repository, a set of historical data is selected corresponding to a historical period, the set of historical data including a historical forecast related to the geographical area, and an actual measurement of wind at a location in the geographical area, the set of historical data having a subset of the set of characteristics. From the set of historical data, a function is computed to represent a discrepancy between the historical forecast and the actual measurement over the historical period. A bias value is computed from the function. By applying the bias to a value in the forecast, an adjusted forecast is produced of winds at a wind turbine located at a location in the geographical area.

Abstract: Embodiments are directed to a computer-implemented method, computer system, and computer program product of forecasting power generation. The method includes analyzing a set of historical power generation data. Thereafter a set of clusters is determined wherein each cluster of the set of clusters represents power generated during the time period. Then receiving actual power generation data for a portion of the time period. Thereafter, determining which cluster of the set of clusters contains historical power generation data that is most similar to the actual power generation data. Then forecasting power generation for a remainder of the time period, using the historical power generation data of the cluster.

Abstract: A method for predicting solar power generation receives historical power profile data and historical weather micro-forecast data at a given location for a set of days. Based on power output features for the days, clusters are generated. A classification model that assigns a day to a generated cluster according to weather features is created. For each cluster, a regression model that takes as input weather features and outputs predicted solar power is built. A system includes a sensor for collecting meteorological data at a solar farm, a meter for measuring photovoltaic power output of the solar farm, and a computer processor for executing instructions to predict solar power generation at the solar farm according to the method disclosed, based on data from the sensor and the meter, for a predefined time period. Further instructions predict solar power generation at the solar farm based on a micro-forecast for the solar farm.

Abstract: A method for automatically determining an Internet home page corresponding to a named entity identified by a specified descriptor including building a trained machine-learning model, generating candidate matches from the specified descriptor, wherein each candidate match includes an Internet address, extracting content-based features from websites associated with the Internet addresses of the candidate matches, determining a model score for each candidate match based on the content-based features using the trained machine-learning model, and determining a match from among the candidate matches according to the scores, wherein the match is returned as the Internet home page corresponding to the named entity.

Abstract: A method for automatically determining an Internet home page corresponding to a named entity identified by a specified descriptor including building a trained machine-learning model, generating candidate matches from the specified descriptor, wherein each candidate match includes an Internet address, extracting content-based features from websites associated with the Internet addresses of the candidate matches, determining a model score for each candidate match based on the content-based features using the trained machine-learning model, and determining a match from among the candidate matches according to the scores, wherein the match is returned as the Internet home page corresponding to the named entity.

Abstract: A system and method for detecting trends in time-managed lifetime data for products shipped in distinct vintages within a time window. Data is consolidated from several sources and represented in a form amenable to detection of trends using a criterion for measuring failure. A weight is applied to the failure measures, the weight increasing over the time the products are in the time window. A function of weighted failures is used to define a severity index for proximity to an unacceptable level of failures, and an alarm signal is triggered at a threshold level that allows the level of false alarms to be pre-set.

Abstract: A system and method for detecting trends in time-managed lifetime data for products shipped in distinct vintages within a time window. Data is consolidated from several sources and represented in a form amenable to detection of trends using a criterion for measuring failure. A weight is applied to the failure measures, the weight increasing over the time the products are in the time window. A function of weighted failures is used to define a severity index for proximity to an unacceptable level of failures, and an alarm signal is triggered at a threshold level that allows the level of false alarms to be pre-set.