Patents by Inventor Srivats Shukla
Srivats Shukla has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240056027Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.Type: ApplicationFiled: May 12, 2023Publication date: February 15, 2024Applicant: Utopus Insights, Inc.Inventors: Srivats Shukla, Younghun Kim, Aijun Deng
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Patent number: 11689154Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.Type: GrantFiled: August 31, 2021Date of Patent: June 27, 2023Assignee: Utopus Insights, Inc.Inventors: Srivats Shukla, Younghun Kim, Aijun Deng
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Publication number: 20210405253Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.Type: ApplicationFiled: August 31, 2021Publication date: December 30, 2021Applicant: Utopus Insights, Inc.Inventors: Srivats Shukla, Younghun Kim, Aijun Deng
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Patent number: 11105958Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.Type: GrantFiled: December 28, 2018Date of Patent: August 31, 2021Assignee: Utopus Insights, Inc.Inventors: Srivats Shukla, Younghun Kim, Aijun Deng
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Publication number: 20210175715Abstract: An example method comprises receiving an initial topology of an electrical, receiving a selection of a region of interest, determining one or more external equivalents of the electrical network that are external to the region of interest, determining one or more internal equivalents of the region of interest, calculating a sensitivity matrix based on electrical impedances of at least one of the one or more internal equivalents and based on an amount of power exchanged when in operation, determining a subset of the sensitivity matrix as indicating highly sensitive buses, receiving historical data regarding power flows, predicting power flow for each highly sensitive buses, comparing the predicted power flow to at least one predetermined threshold to determine possible network congestion, and generating a report regarding network congestion and locations of possible network congestion in the region of interest based on the comparison.Type: ApplicationFiled: February 12, 2021Publication date: June 10, 2021Inventors: Srivats Shukla, Mark Gang Yao, Mark A. Lavin, Ronald Ambrosio
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Patent number: 11017315Abstract: A method includes training a prediction model to forecast a likelihood of curtailment for at least one wind turbine. The prediction model is trained, by a processor system, using historical information and historical instances of curtailment. The method also includes forecasting the likelihood of curtailment for the at least one wind turbine using the trained prediction model. The method also includes outputting the forecasted likelihood.Type: GrantFiled: March 22, 2017Date of Patent: May 25, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Younghun Kim, Srivats Shukla, Lloyd A. Treinish
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Patent number: 10923915Abstract: An example method comprises receiving an initial topology of an electrical, receiving a selection of a region of interest, determining one or more external equivalents of the electrical network that are external to the region of interest, determining one or more internal equivalents of the region of interest, calculating a sensitivity matrix based on electrical impedances of at least one of the one or more internal equivalents and based on an amount of power exchanged when in operation, determining a subset of the sensitivity matrix as indicating highly sensitive buses, receiving historical data regarding power flows, predicting power flow for each highly sensitive buses, comparing the predicted power flow to at least one predetermined threshold to determine possible network congestion, and generating a report regarding network congestion and locations of possible network congestion in the region of interest based on the comparison.Type: GrantFiled: March 13, 2018Date of Patent: February 16, 2021Assignee: Utopus Insights, Inc.Inventors: Srivats Shukla, Mark Gang Yao, Mark A. Lavin, Ronald Ambrosio
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Publication number: 20200209430Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.Type: ApplicationFiled: December 28, 2018Publication date: July 2, 2020Applicant: Utopus Insights, Inc.Inventors: Srivats Shukla, Younghun Kim, Aijun Deng
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Patent number: 10521525Abstract: 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.Type: GrantFiled: January 23, 2017Date of Patent: December 31, 2019Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Aanchal Goyal, Fook-Luen Heng, Younghun Kim, Tarun Kumar, Mark A. Lavin, Srivats Shukla, Wander S. Wadman, Kevin Warren
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Publication number: 20190288514Abstract: An example method comprises receiving an initial topology of an electrical, receiving a selection of a region of interest, determining one or more external equivalents of the electrical network that are external to the region of interest, determining one or more internal equivalents of the region of interest, calculating a sensitivity matrix based on electrical impedances of at least one of the one or more internal equivalents and based on an amount of power exchanged when in operation, determining a subset of the sensitivity matrix as indicating highly sensitive buses, receiving historical data regarding power flows, predicting power flow for each highly sensitive buses, comparing the predicted power flow to at least one predetermined threshold to determine possible network congestion, and generating a report regarding network congestion and locations of possible network congestion in the region of interest based on the comparison.Type: ApplicationFiled: March 13, 2018Publication date: September 19, 2019Applicant: Utopus Insights, Inc.Inventors: Srivats Shukla, Mark Gang Yao, Mark A. Lavin, Ronald Ambrosio
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Publication number: 20180276554Abstract: A method includes training a prediction model to forecast a likelihood of curtailment for at least one wind turbine. The prediction model is trained, by a processor system, using historical information and historical instances of curtailment. The method also includes forecasting the likelihood of curtailment for the at least one wind turbine using the trained prediction model. The method also includes outputting the forecasted likelihood.Type: ApplicationFiled: March 22, 2017Publication date: September 27, 2018Inventors: Younghun Kim, Srivats Shukla, Lloyd A. Treinish
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Publication number: 20180210976Abstract: 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.Type: ApplicationFiled: January 23, 2017Publication date: July 26, 2018Inventors: Aanchal Goyal, Fook-Luen Heng, Younghun Kim, Tarun Kumar, Mark A. Lavin, Srivats Shukla, Wander S. Wadman, Kevin Warren