Bing Dong

Professor

Mechanical and Aerospace Engineering

365 Link Hall

bidong@syr.edu

315.443.1293

Degrees:

  • Ph.D. in Building Performance and Diagnostics, Carnegie Mellon University
  • M.S. in Building Science, National University of Singapore
  • B.E. in Electrical and Mechanical Engineering, Nanjing University of Technology

Lab/Center Affiliations:

  • Built Environment Science and Technology (BEST) Lab
  • Syracuse Center of Excellence in Energy and Environmental Systems

Research interests:

  • Modeling occupant behavior in buildings
  • Intelligent building operation
  • Fault detection and diagnostics
  • Buildings-to-grid integration
  • Grid-interactive Efficient Buildings
  • Urban mobility
  • Urban building energy modeling
  • Modeling and optimization of urban energy system
  • Human performance

Current Research:

Prof. Dong’s current research goal is to explore how smart buildings play an active role in urban scale cyber-physical energy system considering human behavior, renewable energy, energy storage, smart grid, health and resilience through physics-based modeling, optimization and controls, heterogeneous sensing and data-driven models. Current major research topics are: (1) Human-Building-Interactions including Detecting, Modeling and Simulating Occupant Behavior in Buildings and Behavior-driven Control and Optimization for Energy Systems and (2) System-level Modeling, Optimization and Control for Urban Built Environment including Buildings-to-Grid Integration Control and Optimization Framework, Modeling of Occupancy Behavior at a Community Level and Connect with other Urban Infrastructures and Community energy planning and management.

Major ongoing research projects are (1) NSF CAREER: Holistic Assessment of the Impacts of Connected Buildings and People on Community Energy Planning and Management, (2) Department of Energy – Argonne National Lab: Spatial-temporal data-driven weather and energy forecasting for improved implementation of advanced building controls, and (3) ARPA-E: Quantification of HVAC Energy Savings for Occupancy Sensing in Buildings through An Innovative Testing Methodology.

Teaching Interests:

  • HVAC design
  • Building performance modeling and diagnostics

Honors and Awards:

  • 2023 IBPSA World Fellow
  • 2023 ASHRAE Distinguished Service Award (DSA)
  • 2023 Best Paper Awards (Journals of Building and Environment, Building Simulations)
  • 2019 NSF CAREER Award
  • 2018 IBPSA-USA Emerging Contributor Award

Select Publications:

Jiang, Z. and Dong, B., 2024. Modularized neural network incorporating physical priors for future building energy modeling. Patterns.

Wang, X. and Dong, B., 2024. Long-term experimental evaluation and comparison of advanced controls for HVAC systems. Applied Energy371, p.123706.

Liu, Y. and Dong, B., 2024, January. Modeling urban scale human mobility through big data analysis and machine learning. In Building Simulation (Vol. 17, No. 1, pp. 3-21). Beijing: Tsinghua University Press.

Liu, Y., Dong, B., Hong, T., Olesen, B., Lawrence, T. and O’Neill, Z., 2023. ASHRAE URP-1883: Development and Analysis of the ASHRAE Global Occupant Behavior Database. Science and Technology for the Built Environment29(8), pp.749-781.

Deng, Z., Wang, X. and Dong, B., 2023. Quantum computing for future real-time building HVAC controls. Applied Energy334, p.120621.

Wang, X., Dong, B. and Zhang, J.J., 2023, February. Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season. In Building Simulation (Vol. 16, No. 2, pp. 205-223). Beijing: Tsinghua University Press.

Wu, W., Dong, B., Wang, Q.R., Kong, M., Yan, D., An, J. and Liu, Y., 2020. A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption. Applied Energy278, p.115656.

Wagner, A., O’Brien, W. and Dong, B. eds., 2018. Exploring Occupant Behavior in Buildings: Methods and Challenges. Springer.

Dong, B., Li, Z.*, Taha, A. and Gatsis, N., 2018. Occupancy-based buildings-to-grid integration framework for smart and connected communities. Applied Energy, 219, pp.123-137.(IF: 7.182)