Patents by Inventor Kurandwad SAGAR

Kurandwad SAGAR 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).

  • Method and system for adaptive forecast of wind resources
    Patent number: 9269056
    Abstract: A method and system are provided for determining at least one combined forecast value of non-conventional energy resources. An Input/output Interface receives an adaptively selected historical dataset and a current dataset from one or more predictive forecast models and/or measurements. An adaptive forecast module generates one or more variants of machine learning models to model the performance of the one or more predictive forecast models by training the one or more variants of machine learning models on the historical dataset. The adaptive forecast module correlates the current dataset with the historical dataset to adaptively obtain a filtered historical dataset. The adaptive forecast module evaluates the one or more variants of machine learning models on the filtered historical dataset. The adaptive forecast module derives a statistical model to determine the at least one combined forecast value by combining outputs obtained based on the evaluation.
    Type: Grant
    Filed: July 17, 2013
    Date of Patent: February 23, 2016
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Venkata Ramakrishna Padullaparthi, Kurandwad Sagar, Geetha Thiagarajan, Anand Sivasubramaniam
  • METHOD AND SYSTEM FOR ADAPTIVE FORECAST OF WIND RESOURCES
    Publication number: 20140025354
    Abstract: A method and system are provided for determining at least one combined forecast value of non-conventional energy resources. An Input/output Interface receives an adaptively selected historical dataset and a current dataset from one or more predictive forecast models and/or measurements. An adaptive forecast module generates one or more variants of machine learning models to model the performance of the one or more predictive forecast models by training the one or more variants of machine learning models on the historical dataset. The adaptive forecast module correlates the current dataset with the historical dataset to adaptively obtain a filtered historical dataset. The adaptive forecast module evaluates the one or more variants of machine learning models on the filtered historical dataset. The adaptive forecast module derives a statistical model to determine the at least one combined forecast value by combining outputs obtained based on the evaluation.
    Type: Application
    Filed: July 17, 2013
    Publication date: January 23, 2014
    Inventors: Venkata Ramakrishna PADULLAPARTHI, Kurandwad SAGAR, Geetha THIAGARAJAN, Anand SIVASUBRAMANIAM