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    Multi-agent deep reinforcement learning for dynamically controlling electrical equipment in buildings
    2.
    发明授权
    Multi-agent deep reinforcement learning for dynamically controlling electrical equipment in buildings 有权

    公开(公告)号：US12111620B2

    公开(公告)日：2024-10-08

    申请号：US17029788

    申请日：2020-09-23

    Applicant: Tata Consultancy Services Limited

    Inventor： Srinarayana Nagarathinam ,  Avinash Achar ,  Arunchandar Vasan

    IPC: G05B13/02 ,  F24F11/62 ,  G05B15/02 ,  G06N3/088

    CPC classification number: G05B13/027 ,  F24F11/62 ,  G05B15/02 ,  G06N3/088

    Abstract: Reinforcement Learning agent interacting with a real-world building to determine optimal policy may not be viable due to comfort constraints. Embodiments of the present disclosure provide multi-deep agent RL for dynamically controlling electrical equipment in buildings, wherein a simulation model is generated using design specification of (i) controllable electrical equipment (or subsystem) and (ii) building. Each RL agent is trained using simulation model and deployed in the subsystem. Reward function for each subsystem includes some portion of reward from other subsystem(s). Based on reward function of each RL agent, each RL agent learns an optimal control parameter during execution of RL agent in subsystem. Further, a global optimal control parameter list is generated using the optimal control parameter. The control parameters in the global optimal control parameters list are fine-tuned to improve subsystem's performance. Information on fine-tuning parameters of the subsystem and reward function are used for training RL agents.

    Systems and methods for optimizing incentives for demand response
    3.
    发明授权
    Systems and methods for optimizing incentives for demand response 有权

    公开(公告)号：US11301941B2

    公开(公告)日：2022-04-12

    申请号：US15804337

    申请日：2017-11-06

    Applicant: Tata Consultancy Services Limited

    Inventor： Kundan Kandhway ,  Arunchandar Vasan ,  Srinarayana Nagarathinam ,  Venkatesh Sarangan ,  Anand Sivasubramaniam

    IPC: G06Q50/06 ,  G06Q30/02 ,  G06F30/13 ,  G06F119/08

    Abstract: Electrical utilities offer incentives to customers to reduce consumption during periods of demand-supply mismatch. A building's participation in demand response (DR) depends both on its ability (due to building constraints), and its willingness (a function of incentive) to reduce electricity. Customers prefer a large incentive whereas a utility would want to minimize the revenue outflow to achieve a target reduction. Systems and methods of the present disclosure identify optimal incentive from the utility's perspective reflecting this trade-off. A model is built to estimate the demand response potential (DRP) of a building for a given incentive offered by the utility. The models for individual buildings are used to characterize the behavior of an ensemble of buildings. The utility may then decide optimum incentives that should be offered to achieve a target DR, using the associated DRP.

    LEAK LOCALIZATION IN WATER DISTRIBUTION NETWORKS
    4.
    发明申请
    LEAK LOCALIZATION IN WATER DISTRIBUTION NETWORKS 审中-公开
    水分配网络中的泄漏本地化

    公开(公告)号：US20160349141A1

    公开(公告)日：2016-12-01

    申请号：US15112246

    申请日：2015-02-17

    Applicant: Tata Consultancy Services Limited

    Inventor： Venkatesh Sarangan ,  Iyswarya Narayanan ,  Arunchandar Vasan ,  Anand Sivasubramaniam

    IPC: G01M3/28 ,  E03B1/02 ,  E03B7/00 ,  G01F1/00

    CPC classification number: G01M3/2807 ,  E03B1/02 ,  E03B7/003 ,  G01F1/00

    Abstract: Described herein, are methods and systems for locating a leak in a water distribution network. According to an implementation, a leak situation in the water distribution network is detected based on a flow difference value between an actual flow value and a predicted flow value of an inlet flow meter of the water distribution network at at least one time interval. Leak signature values of demand nodes in the water distribution network at the at least one time interval are determined. A leak signature value of a respective demand node at a respective time interval is determined based on centrality metrics, the predicted flow value at the respective time interval, and static physical properties related to the water distribution network. At least one possible leak node is identified based on the flow difference value and the leak signature values of the demand nodes at the at least one time interval.

    Abstract translation: 这里描述的是用于定位配水网络中的泄漏的方法和系统。 根据实施方式，基于水分配网络的入口流量计的实际流量值和预测流量值之间的流量差在至少一个时间间隔来检测配水网络中的泄漏情况。 确定在至少一个时间间隔的配水网络中的需求节点的签名值。 基于中心度量，各时间间隔的预测流量值和与配水网络相关的静态物理特性来确定相应时间间隔处的相应需求节点的泄漏签名值。 基于在至少一个时间间隔的需求节点的流量差值和泄漏签名值来识别至少一个可能的泄漏节点。

    Methods and systems for benchmarking asset performance
    5.
    发明授权
    Methods and systems for benchmarking asset performance 有权

    公开(公告)号：US11609156B2

    公开(公告)日：2023-03-21

    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: G06Q10/04 ,  G01M99/00 ,  G06Q10/0639

    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.

    Method and system for maximizing space utilization in a building
    9.
    发明授权
    Method and system for maximizing space utilization in a building 有权

    公开(公告)号：US11781773B2

    公开(公告)日：2023-10-10

    申请号：US17195699

    申请日：2021-03-09

    Applicant: Tata Consultancy Services Limited

    Inventor： Praveen Manoharan ,  Srinarayana Nagarathinam ,  Arunchandar Vasan ,  Venkata Ramakrishna Padullaparthi

    IPC: F24F11/63 ,  F24F11/46 ,  F24F140/60 ,  F24F140/50 ,  F24F120/12

    CPC classification number: F24F11/63 ,  F24F11/46 ,  F24F2120/12 ,  F24F2140/50 ,  F24F2140/60

    Abstract: This disclosure relates generally to method and system for maximizing space utilization in a building. Due to current pandemic scenario many organizations eventually need to plan for the return of employees to office space ensuring biosafety. The challenge of maximizing the office space utilization ensuring occupants biosafety and comfort thereby minimizing HVAC energy consumption is necessary. The method utilizes two heuristic approaches for determining maximum allowable occupants placement in the open plan space using an optimal occupant placement technique. This minimizes the HVAC energy if the actual count is lesser than the possible maximum occupants can be placed which further optimizes energy using a joint actuator control technique. Additionally, the proposed two heuristic approaches improve space utilization for the infection rate ensuring bio safety. Full utilization of open plan space is possible when the community infection rate and exposure duration are relatively low resulting low risk probability for uninfected occupants.

    Methods and systems for fault detection, diagnosis and localization in solar panel network
    10.
    发明授权
    Methods and systems for fault detection, diagnosis and localization in solar panel network 有权

    公开(公告)号：US11251749B2

    公开(公告)日：2022-02-15

    申请号：US17073010

    申请日：2020-10-16

    Applicant: Tata Consultancy Services Limited

    Inventor： Venkata Ramakrishna Padullaparthi ,  Sneha Mary Thumma ,  Arunchandar Vasan

    IPC: H02S50/10 ,  G06F30/27 ,  G06F111/10

    Abstract: Various fault types occurring at multiple possible locations in the solar panel network are simulated using the network simulation model. The dataset covering multiple fault scenarios and multiple no-fault scenarios is determined for training the CNN model. The fault scenarios include one fault type alone at particular location or multiple locations, as well as multiple fault types at multiple locations. The fault types include a short circuit fault, an open circuit fault, a shading fault, a soiling fault, a hot-spot fault, an arc fault, a degradation fault, and a clipping fault, the short circuit fault comprises a line-line fault, and a line-ground fault The convolutional neural network (CNN) model is trained with fault datasets and no-fault datasets covering various fault sensors and no-fault scenarios to generate the FDDL model. The fault datasets and no-fault datasets are determined based on the network simulation model of the solar panel network.

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