Tata Consultancy Services Limited

Rajesh Jayaprakash

Anand Sivasubramaniam

Arunchandar Vasan

Srinarayana Nagarathinam

Venkatesh Ramanathan

Amarendra Kumar Singh

Harshad Girish Bhagwat

Supriya Agrawal

Ramasubramanian Suriyanarayanan

Sankara Narayanan D

Venkatesh Sarangan

Umesh Singh

Sagar Kumar Verma

Perumal Rajaram

Pandeeswari Indumathi Sankaranarayanan

Sivabalan Thirunavukkarasu

Praveen Orvakanti

Rajesh Subbiah

Arun Varghese

Arvind Ramanujam

Avinash Achar

Ulka Shrotri

Rohith Regikumar

Aabriti Dutta

Priyanga Kasthurirajan

HVAC control system's supervisory control is crucial for energy-efficient thermal comfort in buildings. The control logic is usually specified as 'if-then-that-else' rules that capture the domain expertise of HVAC operators, but they often have conflict that may lead to sub-optimal HVAC performance. Embodiments of the present disclosure provide a method and system for optimized Heating, ventilation, and air-conditioning (HVAC) control using domain knowledge combined with Deep Reinforcement Learning (DRL). The system disclosed utilizes Deep Reinforcement Learning (DRL) for conflict resolution in a HVAC control in combination with domain knowledge in form of control logic. The domain knowledge is predefined in an Expressive Decision Tables (EDT) engine via a formal requirement specifier consumable by the EDT engine to capture domain knowledge of a building for the HVAC control.

Optimized HVAC control using domain knowledge combined with deep reinforcement learning (DRL)

Vehicle-to-Grid (V2G) technologies are being adopted to reduce peak demand and to take over as energy sources during grid instability. It is necessary to estimate attributes of electric vehicle trips and residual battery charge in order to correctly predict spatio-temporal availability of energy from EVs to form a micro grid. However, it may not be feasible to get the required attributes for all vehicles in a city-scale traffic scenario. Embodiments of the present disclosure and system address the problem of accurately estimating the local energy reserve that is available from parked EVs during a given time of the day. In addition, the system also determines which neighborhoods have the potential to form micro grids using the parked EVs during a given time period. This will help grid operator(s) to plan and design smart grids which can create EV-powered micro grids in neighborhoods during periods of peak demand or during disruptions.

Electric vehicle based micro grid identification for on-demand power supply

Sub-systems of air handling units in infrastructures face unresolved problem of conflict in the rules that activate in a contradictory manner at the same time resulting in sub-optimal performance of the subsystems. The present disclosure provides a system and method for optimizing performance parameters of air handling units in infrastructures. Rule sets having conflicting conditions are identified after verification of rules which are specific to air handling units. Further, frequency of the rule sets having conflicting conditions is determined to generate a ranked list of the rule sets having conflicting conditions. Another ranking procedure is implemented for the rules comprised in the ranked list of the rule sets having conflicting conditions. The system dynamically optimizes one or more parameters specific to the performance criteria based on the ranking of rules.

Systems and methods for optimizing performance parameters of air handling units in infrastructures

Accurate estimation of the trajectory of a vehicle by selecting optimal number of GPS data points and a shortest path technique applied for estimation is important and crucial. Method and system for estimating a trajectory from GPS data points is described. The method disclosed utilizes a plurality of GPS data points of a vehicle, an existing road map and a set of equal time intervals obtained by dividing an elapsed time during movement of the vehicle. Each GPS data point is associated to a time interval and a set of candidate points are mapped to each GPS data point correspondingly. A set of possible paths are determined between the set of candidate points in each time interval to estimate the trajectory of the vehicle using one of a shortest path technique and an edit distance technique.

Method and system for estimating a trajectory from GPS data points

Identifying policy violations for controlling behavior of an Internet of Things (IoT) automation system is provided. Traditional systems and methods provide for controlling IoT based automation system based upon a static analysis of a system model and rules. The embodiments of the proposed disclosure provide for controlling behavior of the IoT automation system by identifying one or more policy violations, wherein the one or more policy violations are identified by generating a plurality of models representing behavior, relationships and functions of one or more sub-systems corresponding to the IoT automation system; extracting a set of modelled rules; constructing, using each of the plurality of models and the set of modelled rules, an integrated model; and identifying, from the integrated model, the one or more policy violations via a Model Verifier Component for controlling behavior of the IoT automation system.

Controlling behavior of an internet of things (IoT) automation system by identifying policy violations

This disclosure relates generally to a system and method to identify at least one conflict and for controlling both static and dynamic variables in one or more operations of at least one subsystem of a plurality of building automation sub-systems. It includes a supervisory control layer that orchestrates multiple underlying sub-systems like heating, ventilation, and air-conditioning (HVAC) sub-systems and at least one access control sub-system. A test case generation framework is used to verify static and dynamic variables of operations of the sub-systems. It identifies conflicts in the static and dynamic variables. Therefore, the system provides controls to the sub-systems using the identified and adjusted conflict of static and dynamic variables on operations.

System and method for efficient verification of building automation systems

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.

Method and system providing temporal-spatial prediction of load demand

Disclosed is a system and method for optimizing cooling efficiency of a data center is disclosed. The system may comprise an importing module, a Computational fluid dynamics (CFD) modeling module, a scope determination module, a metrics computation module, an identification module and a recommendation module. The importing module may be configured to import data associated to the data center. The CFD modeling module may be configured to leverage an external CFD Analysis tool in order to develop a CFD model of the data center. The scope determination module may be configured to determine a scope for optimizing the cooling efficiency of the data center. The metrics computation module may be configured to compute metrics based upon the data. The identification module may be configured to identify inefficiency and a cause producing the inefficiency. The recommendation module may be configured to facilitate optimizing cooling efficiency of the data center.

System and method for optimizing cooling efficiency of a data center

Disclosed is a system and method for facilitating optimizing cooling efficiency of a data center. The method may comprise receiving a layout of the data center. The method may comprise computing co-ordinates of each equipment of a plurality of equipments associated with the data center. Further, the method may comprise segregating the layout into a plurality of cells. The method may comprise capturing preliminary data associated with the data center. Further, the method may comprise determining a state value of the data center based upon the preliminary data. The method may comprise capturing CFD data and, selectively, thermal assessment data. Further, the method may comprise facilitating the optimization of the cooling efficiency of the data center by using an external analysis tool capable of performing Computational Fluid Dynamics (CFD) analysis or thermal assessment followed by the Computational Fluid Dynamics (CFD) analysis using the CFD data and the thermal assessment data.

System and method for facilitating optimization of cooling efficiency of a data center

The subject matter described herein is directed to a power monitoring system for managing power in a data center. In one embodiment, the power monitoring system includes: at least one processor, a memory coupled to the at least one processor, wherein the memory includes, a computation module configured to compute at least one of peak power consumption and current power consumption of each power distribution point of a power distribution unit and an analysis module configured to identify the power distribution points which are overloaded or are underutilized based on a policy data.

Power monitoring system

The application provides a method and system for real time monitoring and prediction of temperatures in a data center for effective thermal management. Further, the invention provides a method and system for analyzing and display of monitored and predicted temperatures for obtaining complete temperature profile of the data center.

Method and system for real time monitoring, prediction, analysis and display of temperatures for effective thermal management in a data center

The subject matter described herein relates to a system and a method for generation of energy consumption profiles corresponding to a plurality of computing systems. For each of the plurality of the computing systems, a plurality of consumption parameters from at least one measurement device is received. The consumption parameters include a processor utilization parameter and an energy consumption parameter. Further, a normalization factor corresponding to each of the plurality of the computing systems is identified. Based on the normalization factor, the processor utilization parameter is normalized. Based on the normalized processor utilization parameter and the energy consumption parameter, the energy consumption profile is generated. The energy consumption profile is indicative of energy efficiency of the plurality of the computing systems.

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Chennai, India

Rajesh Jayaprakash