Rohit Chintala is a researcher in the Residential Buildings Research Group at NREL. His research primarily focuses on fault detection and diagnosis, building energy modeling, and developing advanced control methodologies for greater adoption of renewable energy resources. His background is in system identification, control theory, machine learning, and mathematical optimization.

Prior to working at NREL, Rohit was a doctoral student in the Mechanical Engineering Department of Texas A&M University, where he proposed an automated method for developing thermodynamic models of multi-zone buildings at a large scale for implementing advanced controls such as model predictive control.

Research Interests

System Identification

Model Predictive Control

Machine Learning

Education

Ph.D. Mechanical Engineering, Texas A&M University

M.S. Mechanical Engineering, Texas A&M University

Featured Work

Distributed model predictive control for coordinated, grid-interactive buildings Applied Energy (2022)

Automated fault detection of residential air-conditioning systems using thermostat drive cycles, Energy and Buildings (2021)

Identification and Elimination of Hunting Behavior in HVAC Systems, ASHRAE Transactions (2015)

Automated Multi-Zone Linear Parametric Black Box Modeling Approach for Building HVAC Systems, ASME Dynamic Systems and Control Conference (2015)