Patents by Inventor Malini POONI VENKAT

Malini POONI VENKAT 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).

  • Publication number: 20240167713
    Abstract: Use of Physics Informed Neural networks (PINNs) to control building systems is non-trivial, as basic formalism of PINNs is not readily amenable to control problems. Specifically, exogenous inputs (e.g., ambient temperature) and control decisions (e.g., mass flow rates) need to be specified as functional inputs to the neural network, which may not be known a priori. The input feature space could be very high dimensional depending upon the duration (monthly, yearly, etc.) and the (min-max) range of the inputs. The disclosure herein generally relates to Heating, Ventilation, and Air-Conditioning (HVAC) equipment, and, more particularly, to method and system for physics aware control of HVAC equipment. The system generates a neural network model based on a plurality of exogeneous variables from the HVAC. The generated neural network model is then used to generate the one or more control signal recommendations, which are further used to control operation of the HVAC.
    Type: Application
    Filed: October 31, 2023
    Publication date: May 23, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: SRINARAYANA NAGARATHINAM, YASHOVARDHAN SUSHIL CHATI, ARUNCHANDAR VASAN, MALINI POONI VENKAT
  • Publication number: 20210303998
    Abstract: Conventionally, chiller power consumption has been optimized by using Cooling Load based Control (CLC) approach which does not consider impact of a control strategy on other. Embodiments of the present disclosure provide reinforcement learning based control strategy to perform both chiller ON/OFF sequencing as well as setpoint leaving chilled water temperature (LCWT) scheduling. A RL agent is trained using a re-trained transfer learning (TL) model and LCWT, return chilled water temperature of target chillers and ambient temperature of building are read for determining required cooling load to be provided by target chiller(s) based on which target chillers are scheduled for turning ON/OFF. Transfer learning-based approach is implemented by present disclosure to predict power consumed by a chiller at some setpoint by using a model trained on similar chillers which were operated at that setpoint since chillers are usually run at a single setpoint.
    Type: Application
    Filed: December 29, 2020
    Publication date: September 30, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Praveen MANOHARAN, Malini POONI VENKAT, Srinarayana NAGARATHINAM, Arunchandar VASAN