Abstract: A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.

Abstract: Methods, systems, and computer program products for dynamic determination of irrigation-related data using machine learning techniques are provided herein.

Abstract: Methods, systems, and computer program products for dynamic determination of irrigation-related data using machine learning techniques are provided herein.

Abstract: A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.

Abstract: A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.

Abstract: One embodiment provides a method, including: identifying a target farmer having a farm producing a crop; receiving at least one target farmer input related to at least one of: (i) a growing condition of the crop of the target farmer and (ii) a characteristic of the crop; receiving, from each of the plurality of other farmers, additional input; validating the at least one target farmer input by comparing the at least one target farmer input to the additional inputs; generating a reliability score for the target farmer; and producing a ranking for the target farmer with respect to the plurality of other farmers.

Abstract: A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.

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: Disclosed herein are systems, methods, and computer-readable media for utilizing sensor data captured by one or more sensors as well as ground-truth physically sampled data to generate a predictive model that can be used to estimate a measurable parameter without having to obtain additional physically sampled data for the parameter.

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: A system and method to produce an electric network from estimated line impedance and physical line length among smart meter devices is provided using communication between the smart meters. The smart meters: (1) synchronize time using GPS pps signals, which provide an accurate time stamp; (2) send/receive an identifiable signal through the same phase of electric networks; (3) identify other smart meters on the same phase lines by listening to the information signal on the same phase lines; and (4) calculate time-of-arrival of an identifiable signal from other smart meters. The time of arrival information is used to calculate the line length, which is then used to calculate impedance of a line and topology of the electric network. The system then constructs an electric network by combining geo-spatial information and tree-like usual connection information.

Abstract: Disclosed herein are systems, methods, and computer-readable media for utilizing sensor data captured by one or more sensors as well as ground-truth physically sampled data to generate a predictive model that can be used to estimate a measurable parameter without having to obtain additional physically sampled data for the parameter.

Abstract: A system and method to produce an electric network from estimated line impedance and physical line length among smart meter devices is provided using communication between the smart meters. The smart meters: (1) synchronize time using GPS pps signals, which provide an accurate time stamp; (2) send/receive an identifiable signal through the same phase of electric networks; (3) identify other smart meters on the same phase lines by listening to the information signal on the same phase lines; and (4) calculate time-of-arrival of an identifiable signal from other smart meters. The time of arrival information is used to calculate the line length, which is then used to calculate impedance of a line and topology of the electric network. The system then constructs an electric network by combining geo-spatial information and tree-like usual connection information.

Abstract: A system and method to produce an electric network from estimated line impedance and physical line length among smart meter devices is provided using communication between the smart meters. The smart meters: (1) synchronize time using GPS pps signals, which provide an accurate time stamp; (2) send/receive an identifiable signal through the same phase of electric networks; (3) identify other smart meters on the same phase lines by listening to the information signal on the same phase lines; and (4) calculate time-of-arrival of an identifiable signal from other smart meters. The time of arrival information is used to calculate the line length, which is then used to calculate impedance of a line and topology of the electric network. The system then constructs an electric network by combining geo-spatial information and tree-like usual connection information.

Abstract: A system and method to produce an electric network from estimated line impedance and physical line length among smart meter devices is provided using communication between the smart meters. The smart meters: (1) synchronize time using GPS pps signals, which provide an accurate time stamp; (2) send/receive an identifiable signal through the same phase of electric networks; (3) identify other smart meters on the same phase lines by listening to the information signal on the same phase lines; and (4) calculate time-of-arrival of an identifiable signal from other smart meters. The time of arrival information is used to calculate the line length, which is then used to calculate impedance of a line and topology of the electric network. The system then constructs an electric network by combining geo-spatial information and tree-like usual connection information.

Abstract: A system and method to produce an electric network from estimated line impedance and physical line length among smart meter devices is provided using communication between the smart meters. The smart meters: (1) synchronize time using GPS pps signals, which provide an accurate time stamp; (2) send/receive an identifiable signal through the same phase of electric networks; (3) identify other smart meters on the same phase lines by listening to the information signal on the same phase lines; and (4) calculate time-of-arrival of an identifiable signal from other smart meters. The time of arrival information is used to calculate the line length, which is then used to calculate impedance of a line and topology of the electric network. The system then constructs an electric network by combining geo-spatial information and tree-like usual connection information.