Dr. Ali Razban | Energy Managment | Best Researcher Award
Associate Professor | Purdue University | United States
Dr. Ali Razban is a researcher at Indiana University–Purdue University Indianapolis (IUPUI), United States, specializing in intelligent building systems, energy management, and advanced control strategies for Heating, Ventilation, and Air Conditioning (HVAC) systems. With 41 scientific publications and over 700 citations, his work focuses on integrating Internet of Things (IoT) technologies, model predictive control (MPC), and environmental sensing to enhance building performance, occupant comfort, and energy efficiency. His recent studies include privacy-preserving methods for indoor occupancy forecasting, optimized sensor placement for accurate environmental monitoring, and real-world implementation of cloud-based MPC systems for educational buildings. Dr. Razban has also contributed to comprehensive reviews of occupancy detection techniques, addressing their practical challenges in large-scale deployment. His collaborative research spans multiple disciplines—bridging mechanical engineering, computer science, and data analytics—and involves partnerships with over 40 co-authors worldwide. Through both experimental and simulation-based approaches, his work advances sustainable building design and smart infrastructure, contributing to global efforts in reducing energy consumption and carbon emissions. Dr. Razban’s research not only strengthens the academic understanding of intelligent built environments but also delivers practical, scalable solutions for industry applications, thereby fostering the development of resilient, data-driven, and energy-efficient urban ecosystems.
Featured Publication
1. Taheri, S., Hosseini, P., & Razban, A. (2022). Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review. Journal of Building Engineering, 60, 105067.
Cited by: 254
2. Taheri, S., & Razban, A. (2021). Learning-based CO₂ concentration prediction: Application to indoor air quality control using demand-controlled ventilation. Building and Environment, 205, 108164.
Cited by: 194
3. Movahed, P., Taheri, S., & Razban, A. (2023). A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems. Applied Energy, 339, 120948.
Cited by: 60
4. Trautman, N., Razban, A., & Chen, J. (2021). Overall chilled water system energy consumption modeling and optimization. Applied Energy, 299, 117166.
Cited by: 46
5. Hosseini, P., Taheri, S., Akhavan, J., & Razban, A. (2023). Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting. Energy Conversion and Management, 283, 116900.
Cited by: 39
Dr. Ali Razban’s research advances sustainable building technologies through intelligent control, IoT-enabled sensing, and data-driven modeling, reducing energy consumption while improving indoor environmental quality. His work bridges academia and industry to accelerate the transition toward smart, low-carbon infrastructure