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公开(公告)号：US20220154958A1
公开(公告)日：2022-05-19
申请号：US17195699
申请日：2021-03-09
发明人： Praveen MANOHARAN , Srinarayana NAGARATHINAM , Arunchandar VASAN , Venkata Ramakrishna PADULLAPARTHI
摘要： 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.
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2.发明申请公开(公告)号：US20210119576A1
公开(公告)日：2021-04-22
申请号：US17073010
申请日：2020-10-16
摘要： This disclosure relates generally to the methods and systems for fault detection, diagnosis and localization in solar panel network. Conventional fault detection and diagnosis (FDD) techniques for the solar panel network are limited and confined to identifying faults either at voltage level or current level, or to studying one specific fault type at a time. The present disclosure solve the problems of detecting various fault types present inside the solar panel network and identifying associated fault locations, by generating a fault detection, diagnosis and localization (FDDL) model. The convolutional neural network (CNN) model is trained with fault datasets and no-fault datasets covering various fault scenarios and no-fault scenarios respectively, to generate the FDDL model. The plurality of fault datasets and the plurality of no-fault datasets are determined based on the network simulation model of the solar panel network.
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公开(公告)号：US20180357730A1
公开(公告)日：2018-12-13
申请号：US15804337
申请日：2017-11-06
发明人： Kundan KANDHWAY , Arunchandar VASAN , Srinarayana NAGARATHINAM , Venkatesh SARANGAN , Anand SIVASUBRAMANIAM
CPC分类号： G06Q50/06 , G06F17/5004 , G06F2217/80 , G06Q30/0276
摘要： 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.
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公开(公告)号：US20210407300A1
公开(公告)日：2021-12-30
申请号：US17154458
申请日：2021-01-21
摘要： This disclosure relates to a system and method for computing and recommending optimal hold time for every flight of an airline to minimize passenger misconnects in airline operations. The method recommends an optimal hold time for a flight based on passenger and airline disutility. Passenger disutility is computed based on the delay to destination of some or all of the passengers, availability of alternate routes for the connecting passengers, and a passenger specific connection time for connecting passengers. Airline disutility is computed based on the arrival delay of the outbound flight and subsequent flights of the same physical aircraft, till the delay no-longer propagates and an operating cost to the airline such as rebooking cost, ground cost etc. Finally, the method introduces a business factor, bringing in airline specific flexibility, to combine passenger and airline disutility to form total disutility and recommends the optimal hold based on least value.
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公开(公告)号：US20210200163A1
公开(公告)日：2021-07-01
申请号：US17029788
申请日：2020-09-23
摘要： 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.
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6.发明申请公开(公告)号：US20200263893A1
公开(公告)日：2020-08-20
申请号：US16793413
申请日：2020-02-18
发明人： Srinarayana NAGARATHINAM , Harihara Subramaniam MURALIDHARAN , Arunchandar VASAN , Venkatesh SARANGAN , Anand SIVASUBRAMANIAM
IPC分类号： F24F11/46 , F24F11/63 , G05B19/042
摘要： The present disclosure provides system and method for determining optimal decision parameters for a demand response (DR) event involving a District Cooling Plant (DCP). Most of conventional DR event techniques address control of building-level energy consumption loads alone while in presence of District Cooling (DC) has not received much attention when a plurality of buildings are served by a District Cooling Plant (DCP). The disclosed system and method determine set points of optimal decision parameters of the plurality of buildings and the DCP, by conditioning and un-conditioning on the DCP parameters such that a thermal discomfort of occupants residing in the plurality of buildings is minimum and achieves a maximum target energy demand reduction during the DR event. The disclosed system and method work for hundreds of buildings and able to determine the optimal decision parameters for each building and the DCP efficiently.
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公开(公告)号：US20190353690A1
公开(公告)日：2019-11-21
申请号：US15980390
申请日：2018-05-15
发明人： Arvind RAMANUJAM , Pandeeswari SANKARANARAYANAN , Arunchandar VASAN , Rajesh JAYAPRAKASH , Venkatesh SARANGAN , Anand SIVASUBRAMANIAM
摘要： Method and system for predicting temporal-spatial distribution of load demand on an electric grid due to a plurality of Electric Vehicles (EVs) is described. The method includes creating an EV load demand (EVLD) model for a Region of Interest (ROI) serviced by the electric grid, wherein the EVLD model integrates an EV model and a transport simulator simulating EV traffic conditions for the ROI. Further, the method includes computing the load demand in time and space in terms of State of Charge (SOC) of a battery for each EV among the plurality of EVs in the ROI, based on the EVLD model. Furthermore, the method includes aggregating the computed the load demand, in terms of the SOC, of each EV in time domain and space domain to create a temporal-spatial impact of the load demand by the plurality of EVs on the electric grid for the ROI.
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8.发明公开公开(公告)号：US20240183674A1
公开(公告)日：2024-06-06
申请号：US18368818
申请日：2023-09-15
CPC分类号： G01C21/3469 , G06N3/092
摘要： This disclosure relates generally to method and system of electric vehicle route planning for multi-service delivery and on-route energy replenishment. Last mile delivery is a critical component of supply chains that impacts both customer experience and delivery cost. The method disclosed processes a received user request comprising a current location of the user, one or more required services, and a time window rendered between each of the required services and for the user request a graph is generated. Further, the trained learning agent generates a route map indicating a plurality of waypoint locations for the electric vehicle to visit each node in accordance with minimized trip cost of fleet and time duration where each node has a state action pair for the electric vehicle. The learning agent learns continuously during the interaction with the delivery environment and obtains feedback for every associated action.
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9.发明申请公开(公告)号：US20210350278A1
公开(公告)日：2021-11-11
申请号：US17136702
申请日：2020-12-29
发明人： Tejasvi MALLADI , Karpagam MURUGAPPAN , Depak SUDARSANAM , Ramasubramanian SURIYANARAYANAN , Arunchandar VASAN
摘要： Considering the dependency of a flight hold time on multitude of dynamically varying factors, determining an optimal hold time balancing between passenger utility and airline utility is challenging. State of art approaches are limited to use of only deterministic approaches with limited ML assistance that require huge labelled training data. Embodiments disclosed herein provide a method and system for computing and recommending optimal hold time for every flight of an airline so as to minimize passenger misconnects in airline operations through Reinforcement Learning (RL). The method disclosed utilizes RL, which is trained to make decision at a flight level considering local factors while still adhering to the global objective based on global factors. Further method introduces business constants in the rewards to the RL agents bringing in airline specific flexibility in reward function.
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公开(公告)号：US20210303998A1
公开(公告)日：2021-09-30
申请号：US17136957
申请日：2020-12-29
摘要： 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.
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