Rohit Chintala
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View article: Adaptive Computing for Scale-Up Problems
Adaptive Computing for Scale-Up Problems Open
Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocat…
View article: Multi-Fidelity Modeling and Control for Building Temperature Control
Multi-Fidelity Modeling and Control for Building Temperature Control Open
The ability to control energy loads such as a building's heating, ventilation, and air conditioning (HVAC) system can help facilitate increased penetration of variable renewable energy sources into the electric grid. To be able to control …
View article: Adaptive Computing for Scale-up Problems
Adaptive Computing for Scale-up Problems Open
Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocat…
View article: Reinforcement Learning for Building Control and its Real-World Implementation
Reinforcement Learning for Building Control and its Real-World Implementation Open
This presentation compares the performance of reinforcement learning controller with model predictive control for a real-world New York high rise building with 40 floors.
View article: Building Energy Modeling Enhancements to Identify Least-Cost Pathways to Net-Zero Carbon Homes: Preprint
Building Energy Modeling Enhancements to Identify Least-Cost Pathways to Net-Zero Carbon Homes: Preprint Open
Residential grid-interactive efficient buildings (GEBs) can utilize high levels of energy efficiency and demand flexibility to deliver value to occupants, the grid, and society. However, without the ability to analyze, design, and optimize…
View article: Grid-Interactive Electric Vehicle and Building Coordination Using Coupled Distributed Control: Preprint
Grid-Interactive Electric Vehicle and Building Coordination Using Coupled Distributed Control: Preprint Open
As an increasing number of controllable devices are introduced onto the grid, they can individually provide ancillary services in support of grid stability. However, the goals of each device differ due to their type and individual objectiv…
View article: PowerGridworld
PowerGridworld Open
We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training framewo…
View article: Zero Export Feeder Through Transactive Markets
Zero Export Feeder Through Transactive Markets Open
This presentation summarizes how HELICS was used during a collaborative project with Energy Web Foundation and Exelon Corporation. The primary focus of the research was on designing a transactive energy market to accomplish zero export at …
View article: Two-Stage Reinforcement Learning Policy Search for Grid-Interactive Building Control
Two-Stage Reinforcement Learning Policy Search for Grid-Interactive Building Control Open
This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building…
View article: PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems Open
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training framewor…
View article: Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search: Preprint
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search: Preprint Open
In this paper, we develop a grid-interactive multi-zone building controller based on a deep reinforcement learning (RL) approach. The controller is designed to facilitate building operation during normal conditions and demand response even…
View article: PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems [SWR-22-07]
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems [SWR-22-07] Open
NREL's PowerGridworld provides a modular simulation environment for training heterogenous, grid-aware, multi-agent reinforcement learning (RL) policies at scale. The package enables the user to create component gym environments that can be…
View article: Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search Open
In this paper, we develop a grid-interactive multi-zone building controller based on a deep reinforcement learning (RL) approach. The controller is designed to facilitate building operation during normal conditions and demand response even…
View article: Residential Battery Modeling for Control-Oriented Techno-Economic Studies: Preprint
Residential Battery Modeling for Control-Oriented Techno-Economic Studies: Preprint Open
Electrochemical batteries, which serve as electric energy storage devices, are becoming increasingly popular among residential buildings that incorporate solar photovoltaic (PV) systems to help meet their energy needs. Battery economics ar…
View article: Steady-State Predictive Optimal Control of Integrated Building Energy Systems Using a Mixed Economic and Occupant Comfort Focused Objective Function
Steady-State Predictive Optimal Control of Integrated Building Energy Systems Using a Mixed Economic and Occupant Comfort Focused Objective Function Open
Control of energy systems in buildings is an area of expanding interest as the importance of energy efficiency, occupant health, and comfort increases. The objective of this study was to demonstrate the effectiveness of a novel predictive …
View article: A Methodology for Automating the Implementation of Advanced Control Algorithms Such As Model Predictive Control on Large Scale Building HVAC Systems
A Methodology for Automating the Implementation of Advanced Control Algorithms Such As Model Predictive Control on Large Scale Building HVAC Systems Open
Building operations consume about 40% of the total energy consumption in the US, with Heating Ventilation and Air-Conditioning (HVAC) comprising a significant portion of it. HVAC system of a typical commercial building consists of several …