
Bing Dong
Associate Professor
Mechanical & Aerospace Engineering
- 365 Link Hall
- 315.443.1293
- [email protected]
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:
- 2019 NSF CAREER Award
- 2018 IBPSA-USA Emerging Contributor Award
- 2017 Innovator of the Year, The University of Texas at San Antonio
- 2017 Faculty Research Award, The University of Texas at San Antonio
- 2017 Distinguished Service Award for IEA EBC Annex 66 Project
- 2010 Isabel Sophia Liceaga Discretionary Fund Faculty Award, Carnegie Mellon University
- 2009 Akram Midani Award, Carnegie Mellon University
Select Publications:
Wagner, A., O’Brien, W. and Dong, B. eds., 2018. Exploring Occupant Behavior in Buildings: Methods and Challenges. Springer.
Zi, Y. Dong, B., Jin. Y., Da, Y., and Li.Z. Household Appliance Recognition Through a Bayes Classification Model. Sustainable Cities and Society. Accepted. 2019. (IF:1.777)
Mirakhorli, A.* and Dong, B., 2018. Model predictive control for building loads connected with a residential distribution grid. Applied Energy. 230, pp.627-642. (IF: 7.182)
Liu, Y., Yu, N., Wang, W., Guan, X., Xu, Z., Dong, B. and Liu, T., 2018. Coordinating the operations of smart buildings in smart grids. Applied Energy, 228, pp.2510-2525. (IF: 7.182)
Dong, B., Yan, D. Li, Z.*,Jin, Y., Feng, X.H., Fontenot, H. 2018. Modeling occupancy and behavior for better building design and operation—A critical review. In Building Simulation (in Press). Springer Berlin Heidelberg. (IF: 1.170)-Invited Paper: 10 Years Anniversary.
Nayak,T*., Zhang,T., Mao,Z.J., Xu,X.J., Zhang,L., Pack,D., Dong, B. and Huang, Y.F. 2018. Prediction of Human Performance using EEG under Different Indoor Room
Temperatures. Brain Science (IF: 2.6)
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)
Mirakhorli, A.* and Dong, B., 2018. Market and behavior driven predictive energy management for residential buildings. Sustainable Cities and Society, 38, pp.723-735. (IF:1.777)
Li, Z*. and Dong, B., 2018. Short term predictions of occupancy in commercial buildings—Performance analysis for stochastic models and machine learning approaches. Energy and Buildings, 158, pp.268-281.(IF: 4.067)