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)