Patents by Inventor Naveen Kumar Thokala
Naveen Kumar Thokala 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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Patent number: 11961028Abstract: Energy consumption modelling requires to consider various factors affecting the energy consumption in buildings, to be able to effectively forecast future consumption. Even though some of the state of the art deep learning based approaches are able to address these requirements to some extent, they are computationally heavy. The disclosure herein generally relates to energy forecasting, and, more particularly, to a method and system for graph signal processing (GSP) based energy modelling and forecasting. The system monitors and collects information on energy consumption in a building and values of associated energy consumption parameters. This input data is further processed using GSP to generate a building energy consumption model, from which a smooth signal is obtained by applying total variation minimization. The system further performs forecasting using the smooth signal.Type: GrantFiled: January 27, 2022Date of Patent: April 16, 2024Assignee: Tata Consultancy Limited ServicesInventors: Naveen Kumar Thokala, Spoorthy Paresh, Vishnu Brindavanam, Mariswamy Girish Chandra
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Publication number: 20230307907Abstract: This disclosure relates generally to methods and systems for determining the power load disaggregation profile of a building. Most of the conventional techniques are algorithmic centric, specific to certain scenarios and does not employ the low-sampling rate data due to the complexity involved. Present disclosure determines the power load disaggregation profile of the building using the low-sampling rate power consumption data accurately. According to the present disclosure, firstly, the background power loads are detected and removed from the low-sampled data samples. Next, a robust event detection mechanism is employed to detect the events when the change in the power consumption occurred, and such events are paired using the iterative pairing technique. Further, a set of event clusters are formed using the density-based clustering technique and lastly, each of the set of event clusters are classified with each appliance type using a rule-based classification technique.Type: ApplicationFiled: February 28, 2023Publication date: September 28, 2023Applicant: Tata Consultancy Services LimitedInventors: NAVEEN KUMAR THOKALA, SPOORTHY PARESH, JOSE IGNACIO MATEOS ALBIACH, ARUP KUMAR DAS, MARISWAMY GIRISH CHANDRA
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Publication number: 20220327263Abstract: Power consumption forecasting plays a key role in the efficient operation of a building energy management system to assess energy demands of building, and at the same time, help electrical utilities in planning their supply operations. However, no state-of-the-arts are available for forecasting medium-term or long-term power consumption of the buildings. This disclosure relates to a method and system for forecasting a power consumption of buildings for a scalable forecast horizon. The system is configured to pre-process to deal with outliers/missing values, followed by synchronization of smart meter data with other sensory data. An energy-temperature correlation is calculated to estimate an energy drift using historical power consumptions. Further, in feature derivation stage, additional features necessary for the forecast are derived. The system is to be employed for modeling the building load consumption that depends on the time horizon of forecasting and the granularity of the data.Type: ApplicationFiled: March 2, 2022Publication date: October 13, 2022Applicant: Tata Consultancy Services LimitedInventors: NAVEEN KUMAR THOKALA, VISHNU BRINDAVANAM, SPOORTHY PARESH, MARISWAMY GIRISH CHANDRA
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Publication number: 20220237544Abstract: Energy consumption modelling requires to consider various factors affecting the energy consumption in buildings, to be able to effectively forecast future consumption. Even though some of the state of the art deep learning based approaches are able to address these requirements to some extent, they are computationally heavy. The disclosure herein generally relates to energy forecasting, and, more particularly, to a method and system for graph signal processing (GSP) based energy modelling and forecasting. The system monitors and collects information on energy consumption in a building and values of associated energy consumption parameters. This input data is further processed using GSP to generate a building energy consumption model, from which a smooth signal is obtained by applying total variation minimization. The system further performs forecasting using the smooth signal.Type: ApplicationFiled: January 27, 2022Publication date: July 28, 2022Applicant: Tata Consultancy Services LimitedInventors: NAVEEN KUMAR THOKALA, SPOORTHY PARESH, VISHNU BRINDAVANAM, MARISWAMY GIRISH CHANDRA
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Patent number: 11119132Abstract: This disclosure relates generally to method and system for low sampling rate electrical load disaggregation. At low sampling rates, disaggregation of energy load is challenging due to unavailability of events and signatures of the constituent loads. The disclosed energy disaggregation technique receives aggregated load data from a utility meter and sequentially obtains training data for determining disaggregated energy load at low sampling rate. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The obtained dictionary coefficients are treated as graph signals and graph smoothness is used for propagating the coefficients from the training phase to the test phase by formulating an optimization model. The derivation of the optimization model identifies the load of interest and estimate their power consumption based on optimization model constraints. This method achieves accuracy greater than 70% for the loads of interest at low sampling rates.Type: GrantFiled: March 10, 2020Date of Patent: September 14, 2021Assignee: Tata Consultancy Services LimitedInventors: Kriti Kumar, Mariswamy Girish Chandra, Achanna Anil Kumar, Naveen Kumar Thokala
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Publication number: 20210011062Abstract: This disclosure relates generally to method and system for low sampling rate electrical load disaggregation. At low sampling rates, disaggregation of energy load is challenging due to unavailability of events and signatures of the constituent loads. The disclosed energy disaggregation technique receives aggregated load data from a utility meter and sequentially obtains training data for determining disaggregated energy load at low sampling rate. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The obtained dictionary coefficients are treated as graph signals and graph smoothness is used for propagating the coefficients from the training phase to the test phase by formulating an optimization model. The derivation of the optimization model identifies the load of interest and estimate their power consumption based on optimization model constraints. This method achieves accuracy greater than 70% for the loads of interest at low sampling rates.Type: ApplicationFiled: March 10, 2020Publication date: January 14, 2021Applicant: Tata Consultancy Services LimitedInventors: Kriti Kumar, Mariswamy Girish CHANDRA, Achanna Anil KUMAR, Naveen Kumar THOKALA
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Patent number: 10772569Abstract: A device and method is provided for the detection of diabetes in a person using pulse palpation signals. The pulse palpation signal is captured from the radial artery of the person using a photo-plethysmograph (PPG) sensor. The PPG signal is then preprocessed by a processor. The preprocessed PPG signal is then analyzed by the processor to detect the peak in the PPG signal. The detected peaks are used to extract a first set of feature parameters. The first of feature parameters are compared with a second set of feature parameters, wherein the second set of feature parameters are extracted from the control group of individuals. Based on the comparison it is detected that the person is one of in normal condition, pre-diabetic condition or a diabetic condition. According to another embodiment, the invention also provides a method to determine the severity index and progression risk of diabetes in the person.Type: GrantFiled: November 18, 2016Date of Patent: September 15, 2020Assignee: Tata Consultancy Services LimitedInventors: Srinivasan Jayaraman, Naveen Kumar Thokala, Balamuralidhar Purushothaman
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Patent number: 10467533Abstract: System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data.Type: GrantFiled: September 21, 2016Date of Patent: November 5, 2019Assignee: Tata Consultancy Services LimitedInventors: Kriti Kumar, Naveen Kumar Thokala, Ravikumar Karumanchi, Mariswamy Girish Chandra, Kalyan Prathap Kamakolanu Guru, Suresh Upparapalli, Madhusudhan Kamma Chavala Chowdary, Prasanna Madhavrao Kulkarni, Pareshkumar Bhawanishankar Sharda
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Publication number: 20170185902Abstract: System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data.Type: ApplicationFiled: September 21, 2016Publication date: June 29, 2017Applicant: TATA CONSULTANCY SERVICES LIMITEDInventors: Kriti KUMAR, Naveen Kumar Thokala, Ravikumar Karumanchi, Mariswamy Girish Chandra, Kalyan Prathap Kamakolanu Guru, Suresh Upparapalli, Madhusudhan Kamma Chavala Chowdary, Prasanna Madhavrao Kulkarni, Pareshkumar Bhawanishankar Sharda
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Publication number: 20170143279Abstract: A device and method is provided for the detection of diabetes in a person using pulse palpation signals. The pulse palpation signal is captured from the radial artery of the person using a photo-plethysmograph (PPG) sensor. The PPG signal is then preprocessed by a processor. The preprocessed PPG signal is then analyzed by the processor to detect the peak in the PPG signal. The detected peaks are used to extract a first set of feature parameters. The first of feature parameters are compared with a second set of feature parameters, wherein the second set of feature parameters are extracted from the control group of individuals. Based on the comparison it is detected that the person is one of in normal condition, pre-diabetic condition or a diabetic condition. According to another embodiment, the invention also provides a method to determine the severity index and progression risk of diabetes in the person.Type: ApplicationFiled: November 18, 2016Publication date: May 25, 2017Applicant: Tata Consultancy Services LimitedInventors: Srinivasan JAYARAMAN, Naveen Kumar THOKALA, Balamuralidhar PURUSHOTHAMAN
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Patent number: 9477240Abstract: A control module for controlling a thermostatically controlled device includes a processor apparatus adapted to obtain first values for a plurality of parameters for the thermostatically controlled device, the parameters including actual power consumed by the thermostatically controlled device and a number of input parameters, determine a learned correlation function for the thermostatically controlled device based on the obtained values, wherein the learned correlation function relates power consumption of the thermostatically controlled device to at least the number of input parameters, obtain second values for each of the number of input parameters for a future usage period, and determine at least one recommended set point for the thermostatically controlled device using the learned correlation function and at least the second values for each of the number of input parameters.Type: GrantFiled: April 29, 2013Date of Patent: October 25, 2016Assignee: EATON CORPORATIONInventors: Shravana Kumar Musunuri, Naveen Kumar Thokala, Charles J. Luebke, Abhay Shinde
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Publication number: 20140324244Abstract: A control module for controlling a thermostatically controlled device includes a processor apparatus adapted to obtain first values for a plurality of parameters for the thermostatically controlled device, the parameters including actual power consumed by the thermostatically controlled device and a number of input parameters, determine a learned correlation function for the thermostatically controlled device based on the obtained values, wherein the learned correlation function relates power consumption of the thermostatically controlled device to at least the number of input parameters, obtain second values for each of the number of input parameters for a future usage period, and determine at least one recommended set point for the thermostatically controlled device using the learned correlation function and at least the second values for each of the number of input parameters.Type: ApplicationFiled: April 29, 2013Publication date: October 30, 2014Applicant: EATON CORPORATIONInventors: SHRAVANA KUMAR MUSUNURI, Naveen Kumar Thokala, Charles J. Luebke, Abhay Shinde