Rohit Chintala

Rohit Chintala is a Researcher at the National Renewable Energy Laboratory (NREL), affiliated with the Building Technologies and Science Center and the Residential Buildings Research Group. His work focuses on fault detection and diagnosis, building energy modeling, and advanced control methodologies for renewable energy integration.

• Education: PhD and Master's in Mechanical Engineering from Texas A&M University.

His research lies at the intersection of control systems, machine learning, and building technology. He develops scalable thermodynamic models and model predictive control frameworks to enhance energy efficiency in residential buildings, with a focus on renewable energy adoption and electric vehicle integration. His recent work spans distributed computing, multi-fidelity modeling, and real-world implementation of reinforcement learning for building systems.

Key trends in his publications include:

• Advancing adaptive computing for energy system scalability.
• Optimizing building temperature control via multi-fidelity modeling.
• Automated fault detection in HVAC systems using sensitivity analysis.
• Reinforcement learning applications for energy-efficient building operations.

Chintala collaborates with interdisciplinary teams at NREL and contributes to tools like ResStock for nationwide building energy analysis. His work addresses decarbonization challenges through rigorous mathematical optimization and system identification techniques.