Tata Consultancy Services Limited

Supriya Agrawal

Venkatesh Ramanathan

Rajesh Jayaprakash

Srinarayana Nagarathinam

Ulka Shrotri

Sagar Kumar Verma

Anand Sivasubramaniam

Ramasubramanian Suriyanarayanan

Amey Zare

Venkatesh R

Aabriti Dutta

Sagarkumar Verma

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)

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

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

The present disclosure generates a plurality of time bound test cases based on an Artificial Rain Drop (ARD) algorithm. Here, events associated with an event-based system are compiled in a tabular format. Each of the plurality of events are represented as a regular expression. Further, timed finite automaton is constructed for each regular expression prior to applying the ARD algorithm.

Method and system for test case generation for event based systems using graph path traversal

A method and system is provided for automated generation of the functional test cases for testing a software system. In an embodiment, the invention provides an expressive decision table (EDT), a requirement specification notation designed to reduce translation efforts. It implements a novel scalable row-guided random algorithm with fuzzing (RGRaF) (pronounced R-graph) to generate test cases. The invention also implements two new coverage criteria targeted at requirements and requirement interactions. The invention also provides fuzzing at time boundaries to achieve scalability. According to an embodiment, the invention also provides the feature of generating error in case the generated functional test case corresponds to system property violation of the software system. According to another embodiment, the system can also reject the functional test case if there is an improbable condition of the software system.

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Pune, India

Supriya Agrawal