公开(公告)号：US20240167713A1

公开(公告)日：2024-05-23

申请号：US18498980

申请日：2023-10-31

Applicant: Tata Consultancy Services Limited

Inventor： SRINARAYANA NAGARATHINAM ,  YASHOVARDHAN SUSHIL CHATI ,  ARUNCHANDAR VASAN ,  MALINI POONI VENKAT

IPC: F24F11/63 ,  G05B13/02

CPC classification number: F24F11/63 ,  G05B13/027

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.

公开(公告)号：US20200096421A1

公开(公告)日：2020-03-26

申请号：US16578061

申请日：2019-09-20

Applicant: Tata Consultancy Services Limited

Inventor： SRINARAYANA NAGARATHINAM ,  VENKATA RAMAKRISHNA P ,  ARUNCHANDAR VASAN ,  VENKATESH SARANGAN ,  ANAND SIVASUBRAMANIAM

IPC: G01M99/00

Abstract: Traditionally, benchmarking of asset performance involves comparing actual performance with ideal values that correspond to test conditions which may not be realized in practice leading to inappropriate ranking of the assets. Systems and methods of the present disclosure use condition-aware reference curves for estimating the maximum possible operating efficiencies (under specific operating conditions) instead of the theoretical maximum efficiencies. The reference curves are received from the manufacturer or obtained from on-site test results. Benchmarking is then performed based on two dimensions, viz., an inter-asset metric and an intra-asset metric that are analogous to the first law and second law of thermodynamics respectively. The two-dimensional benchmarking then helps in identifying inefficient assets that may be analyzed further for finding the root cause. Tracking the performance of assets over time greatly helps in operations and maintenance, and thus reducing downtime of systems and accordingly the operating costs.

公开(公告)号：US20240384885A1

公开(公告)日：2024-11-21

申请号：US18659996

申请日：2024-05-09

Applicant: Tata Consultancy Services Limited

Inventor： PRASANT KUMAR MISRA ,  AJAY NARAYANAN ,  SRINARAYANA NAGARATHINAM ,  ARUNCHANDAR VASAN

IPC: F24F11/47 ,  B60H1/00 ,  F24F11/64 ,  F24F110/12 ,  F24F120/10 ,  F24F140/60

Abstract: Current approaches for minimizing energy requirement of buildings are not designed to handle multi-input multi-output systems, such as electric vehicle-heating, ventilation, and air conditioning (EV-HVAC) system. Further, scalability of the solutions is another challenge. Present disclosure provides method and system for jointly controlling EV-HVAC system of a building. The system utilizes the potential of electric vehicle (EVs) in building energy management by treating EVs as buffers with random availability. The system performs EV-HVAC joint control that scales seamlessly with increasing EVs while respecting both thermal constraints of HVAC and state of charge (SoC) constraints of EV users.

公开(公告)号：US20250103926A1

公开(公告)日：2025-03-27

申请号：US18823287

申请日：2024-09-03

Applicant: Tata Consultancy Services Limited

Inventor： KSHITIJ GARG ,  PRASANT KUMAR MISRA ,  YOGESH KUMAR BICHPURIYA ,  ARUNCHANDAR VASAN

IPC: G06N5/046 ,  B60L53/65 ,  B60L53/66 ,  G06N3/08

Abstract: This disclosure relates generally to a bidirectional charging at an electric vehicle (EV) charging station by an energy model that uses electricity bought from the day-ahead market for charging the fleet of electric vehicles (EVs) and uses the intra-day market for arbitrage. The competitive pricing of wholesale electricity markets and distributed energy resource capability of EV fleets (in addition) provide a revenue channel through energy arbitrage. To effectively handle electricity price variations and the energy demand of the EV fleet, the present disclosure utilizes a graph representation-based learning agent (LA3_D) with two-stage encoding for day-ahead charge planning; and a priority order based greedy heuristic (GH_I) for intra-day arbitrage planning. Because the agent learns the planning policy of mapping EVs to charging operations over several problem instances, it is able to solve a given instance with limited sub-optimality when put to test at different levels of scale.

公开(公告)号：US20230063075A1

公开(公告)日：2023-03-02

申请号：US17810456

申请日：2022-07-01

Applicant: Tata Consultancy Services Limited

Inventor： PRASANT KUMAR MISRA ,  AJAY NARAYANAN ,  AAKASH KRISHNA GURUVAYUR SASIKUMAR ,  ARUNCHANDAR VASAN ,  VENKATESH SARANGAN

IPC: G06Q30/02

Abstract: This disclosure relates generally to method and system to generate pricing for charging electric vehicles. With Electric vehicles becoming more mainstream, public chargers may not be able to match demand supply without extensive deployments. The present disclosure dynamically generates pricing policies to maximize aggregator revenue based on a stochastic model constraints and user behavioral models. The system involves three primary stake holders comprising a demand side having EV users requesting efficient charging at lowest possible price, a supply side which includes a public or private EVSE operators, and an EV charging aggregator which acts as intermediator between the demand side and the supply side. The EV charging aggregator having an RL agent receives user requests to generate pricing based on a tentative demand pool, an actual demand pool, and a service pool. The reward to the RL agent is the total revenue obtained in that timestep of the control action.