Overview of Model Predictive
Control in Buildings
Tony Kelman
MPC Lab, Berkeley Mechanical Engineering
Email: kelman@berkeley.edu
Tony Kelman
MPC in Buildings
January 11th – Slide 1
Outline
Model predictive control
Basic idea and elements
Advantages, disadvantages
Modeling and MPC in buildings
What works, what doesn’t
Generating models using historical data
Advanced control behavior
Experimental projects
Success stories
Increased scope and capabilities over time
Tony Kelman
MPC in Buildings
January 11th – Slide 2
Model Predictive Control
Use predictive knowledge for control
Basic components
System model – state evolution vs inputs and disturbances
Constraints on inputs or states – requirements, actuator limits
Cost function – reference tracking, energy, comfort
Forecast trajectories of future disturbance inputs –
weather, occupancy, utility rates
Optimization algorithm – fast enough to solve in real time
Advantages: multivariable, model based, nonlinear,
constraint satisfaction, incorporates predictions
Disadvantages: computational complexity, design effort
of accurate modeling
Tony Kelman
MPC in Buildings
January 11th – Slide 3
Model Predictive Control
Initialize with current measurements at time t
Predict response over horizon of p steps
Solve for best input sequence, apply first element u*(t)
Repeat at time t+1 with new measurements (feedback)
Tony Kelman
MPC in Buildings
January 11th – Slide 4
Optimization Formulation
Predicted states xk, inputs uk, disturbances wk
At each time step, solve:
Constrained finite time optimal control problem
Optimization much faster if explicit structure of
J, f, g (and derivatives) can be provided
Tony Kelman
MPC in Buildings
January 11th – Slide 5
Outline
Model predictive control
Basic idea and elements
Advantages, disadvantages
Modeling and MPC in buildings
What works, what doesn’t
Generating models using historical data
Advanced control behavior
Experimental projects
Success stories
Increased scope and capabilities over time
Tony Kelman
MPC in Buildings
January 11th – Slide 6
Modeling for Building Energy Systems
Common practice is black-box simulation
DOE2, EnergyPlus, TRNSYS, etc
Useful for design, very difficult to use for control
Derivative-free optimization not very efficient or scalable
Need model structure for optimization and control
Simpler approach: reduced order modeling
Physics based model structure
Data driven parameter identification
Can adjust accuracy vs complexity tradeoff
Large scale real time optimization tractable
Tony Kelman
MPC in Buildings
January 11th – Slide 7
HVAC Example System
HVAC good target for energy savings by better control
Common configuration for commercial buildings:
VAV with reheat
Control inputs: supply fan, cooling coil, heating coils,
zone dampers, air handling unit dampers
States: zone temperatures
Tony Kelman
MPC in Buildings
January 11th – Slide 8
Thermal Zone Model
Tony Kelman
MPC in Buildings
January 11th – Slide 9
Simplest Useful Model Abstraction
Network of bilinear systems
Thermal zone model
u1
u2
Q_2
Q_1
A
(simple extension to multiple states
per zone, RC network analog)
Q_n
un
Static nonlinearities
Equipment performance maps (chillers, cooling towers, pumps, fans, coils)
Equality and inequality constraints
Comfort range
Dynamic coupling: thermal zones, supply air & return air
Uncertain load predictions
Human: occupancy, thermal comfort, …
Environment: ambient temperature, solar radiation, …
Tony Kelman
MPC in Buildings
January 11th – Slide 10
How to Generate Reduced Models
Several options to create model data
Direct physics based lumped parameters
Model reduction from high fidelity design tools
Use historical data for model identification
Identification results vs measured data, Bancroft library
Tony Kelman
MPC in Buildings
January 11th – Slide 11
Using Data to Quantify Uncertainty
SMPC
Prediction model
Historical load
realization
Load
Ambient temperature
Tony Kelman
MPC in Buildings
January 11th – Slide 12
Advanced Control Behavior
A. Kelman, Y. Ma, A. Daly, F. Borrelli, Predictive Control for Energy Efficient Buildings
with Thermal Storage: Modeling, Stimulation, and Experiments, IEEE Control System
Magazine, 32(1), page 44-64, February 2012.
Time-varying price
Penalize peak power
MPC is able to incorporate time-varying energy price and reduce
peak power consumption
Tony Kelman
MPC in Buildings
January 11th – Slide 13
Outline
Model predictive control
Basic idea and elements
Advantages, disadvantages
Modeling and MPC in buildings
What works, what doesn’t
Generating models using historical data
Advanced control behavior
Experimental projects
Success stories
Increased scope and capabilities over time
Tony Kelman
MPC in Buildings
January 11th – Slide 14
Experimental Projects
UC Merced –, Merced, CA
4% Improvement
LBNL+UTRC- Storage, Chiller Optimization
Horizon 24hrs, Sampling 30min
Problem Size: ~300 variables , ~1440 constraints
CERL Engineering Research Laboratory, Champaign, IL
15% improvement.
UTRC- HVAC distribution – 5 zones
Horizon 4hrs, Sampling 20 min,
Problem Size: ~1600 variables , ~1400 constraints
Naval Station Great Lakes, North Chicago, Illinois
UTRC- Conversion + Storage – 250 zones
Problem Size: ~~20k variables , ~?? constraints
CITRIS Building (UC Berkeley) – Major issues
Siemens - Generation + HVAC distribution -135 Zones
Horizon 4hrs, Sampling 20 min,
Problem Size: ~~10k variables , ~?? constraints
Brower Center (Slab Radiant), Berkeley, CA
Architecture Department
Models based on step tests experiments
White Oak, Silver Spring, MD
Simplified models and
BLOM tool critical for realtime implementation of
large MPC experiments
Honeywell
Microgrid Optimization
Tony Kelman
MPC in Buildings
January 11th – Slide 15
Distributed Implementation
Tony Kelman
MPC in Buildings
January 11th – Slide 16
Distributed Implementation
Coordinator
Dual variables
Supply Fan
Cooling coil
damper
Tony Kelman
Heating coil
VAV damper
Zone
temperature
MPC in Buildings
January 11th – Slide 17