Computational Research Scientist/Engineer
David Blum is a Computational Research Scientist/Engineer in the Building Technology and Urban Systems Division. His research focuses on the development and implementation of next-generation computational tools and workflows for buildings operating in isolation or within broader energy networks. In particular, his interests are in the development, evaluation, and implementation of new control strategies that reduce energy use and cost as well as enable flexible load management important for coordination with electric grids with renewable generation, integration of on-site distributed energy resources, and improving resilience to emergency situations. Much of this work recently has been applied to model predictive control (MPC), where a model of building performance can be used to optimize its energy use, occupant service, and energy network interactions.
David received his B.A.E. degree from the Department of Architectural Engineering at The Pennsylvania State University in 2011 and his M.S. and Ph.D. degrees in Building Technology from the Massachusetts Institute of Technology in 2013 and 2016 respectively. At MIT, his research focused on improving the use of commercial building HVAC systems to provide ancillary services to electric grids through dynamic modeling and MPC. He is a member of the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE), the Institute for Electrical and Electronics Engineers (IEEE), and the International Building Performance Simulation Association (IBPSA).
Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization
Performance comparison of quadratic, nonlinear, and mixed integer nonlinear MPC formulations and solvers on an air source heat pump hydronic floor heating system
Field demonstration and implementation analysis of model predictive control in an office HVAC system
Estimating ASHRAE Guideline 36 energy savings for multi-zone variable air volume systems using Spawn of EnergyPlus
Building optimization testing framework (BOPTEST) for simulation-based benchmarking of control strategies in buildings
Development and Verification of Control Sequences for Single- Zone Variable Air Volume System Based on ASHRAE Guideline 36
Resilient buildings for fire-adapted landscapes: EE and flexible loads integrated with solar and storage microgrids
Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids
An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context
Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems
Optimizing Operational Efficiency: Integrating Energy Information Systems and Model-Based Diagnostics