Comparison of MPC Strategies for Building Control

J. Drgoňa and M. Kvasnica

Slovak University of Technology in Bratislava, Slovakia

June 20, 2013

IAM

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 1 / 18

Motivation

Problem: Almost 40% of final energy use in the world goestowards comfort control in buildings - heating, ventilation and air

conditioning (HVAC).*

In European countries it is over

76% !!!*

Goal: Increasement of building’s energy efficiency

Solution: Building control

*International Energy Agency ‘Energy efficiency requirements in building codes, energy efficiency policies for newbuildings’ 2013 OECD/IEA.

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 2 / 18

Motivation

Problem: Almost 40% of final energy use in the world goestowards comfort control in buildings - heating, ventilation and air

conditioning (HVAC).*

In European countries it is over

76% !!!*

Goal: Increasement of building’s energy efficiency

Solution: Building control

*International Energy Agency ‘Energy efficiency requirements in building codes, energy efficiency policies for newbuildings’ 2013 OECD/IEA.

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 2 / 18

Motivation

Problem: Almost 40% of final energy use in the world goestowards comfort control in buildings - heating, ventilation and air

conditioning (HVAC).*

In European countries it is over

76% !!!*

Goal: Increasement of building’s energy efficiency

Solution: Building control

*International Energy Agency ‘Energy efficiency requirements in building codes, energy efficiency policies for newbuildings’ 2013 OECD/IEA.

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 2 / 18

Motivation

Problem: Almost 40% of final energy use in the world goestowards comfort control in buildings - heating, ventilation and air

conditioning (HVAC).*

In European countries it is over

76% !!!*

Goal: Increasement of building’s energy efficiency

Solution: Building control

*International Energy Agency ‘Energy efficiency requirements in building codes, energy efficiency policies for newbuildings’ 2013 OECD/IEA.

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 2 / 18

Presentation Outline

1 Building Modeling and Control

2 Model Predictive Control

3 Case Study

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 3 / 18

Presentation Outline

1 Building Modeling and Control

2 Model Predictive Control

3 Case Study

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 4 / 18

Building Thermal Control Scheme

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Building

Measurements

Energy minimization

Comfort criteria

OptimizationHeating/Cooling

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 5 / 18

Single Zone Building Model

State Variables

x1− floor temperaturex2− internal facade temperaturex3− external facade temperaturex4− internal temperature

Disturbances

d1− external temperatured2− occupancyd3− solar radiation

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 6 / 18

Single Zone Building Model

State Variables

x1− floor temperaturex2− internal facade temperaturex3− external facade temperaturex4− internal temperature

Disturbances

d1− external temperatured2− occupancyd3− solar radiation

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 6 / 18

Control Objectives

Thermal Comfort

Minimization of Energy Consumption

qx

qu

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 7 / 18

Control Objectives

Thermal Comfort

Minimization of Energy Consumption

qx

qu

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 7 / 18

Control Objectives

Thermal Comfort

Minimization of Energy Consumption

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 7 / 18

Presentation Outline

1 Building Modeling and Control

2 Model Predictive Control

3 Case Study

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 8 / 18

Model Predictive Control

Objective function

minu0,...,uN−1

N−1∑

k=0

ℓ(xk , uk)

Constraints

xk+1 = Axk + Buk + Edk ,

x ≤ xk ≤ x ,

u ≤ uk ≤ u,

x0 = x(t)

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 9 / 18

Model Predictive Control

Objective function

minu0,...,uN−1

N−1∑

k=0

ℓ(xk , uk)

Constraints

xk+1 = Axk + Buk + Edk ,

x ≤ xk ≤ x ,

u ≤ uk ≤ u,

x0 = x(t)

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 9 / 18

Different Formulations of the Objective Function

Reference Tracking + Energy Minimization (Basic)

ℓ(xk , uk) = qx(Cxk − r)2 + quu

2k

r

Comfort Zone Tracking + Energy Minimization (CZT)

ℓ(sk , uk) = qss2k+ quu

2k

s.t. r − ǫ− sk ≤ Cxk ≤ r + ǫ+ sk

+ϵ

-ϵr

Minimization of Zone Violations + Energy Minimization (Hybrid)

ℓ(δk , uk) = qδδk + quu2k

s.t. r − ǫ− sk ≤ Cxk ≤ r + ǫ+ sk(sk > 0) =⇒ (δk = 1)

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 10 / 18

Different Formulations of the Objective Function

Reference Tracking + Energy Minimization (Basic)

ℓ(xk , uk) = qx(Cxk − r)2 + quu

2k

r

Comfort Zone Tracking + Energy Minimization (CZT)

ℓ(sk , uk) = qss2k+ quu

2k

s.t. r − ǫ− sk ≤ Cxk ≤ r + ǫ+ sk

+ϵ

-ϵr

Minimization of Zone Violations + Energy Minimization (Hybrid)

ℓ(δk , uk) = qδδk + quu2k

s.t. r − ǫ− sk ≤ Cxk ≤ r + ǫ+ sk(sk > 0) =⇒ (δk = 1)

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 10 / 18

Different Formulations of the Objective Function

Reference Tracking + Energy Minimization (Basic)

ℓ(xk , uk) = qx(Cxk − r)2 + quu

2k

r

Comfort Zone Tracking + Energy Minimization (CZT)

ℓ(sk , uk) = qss2k+ quu

2k

s.t. r − ǫ− sk ≤ Cxk ≤ r + ǫ+ sk

+ϵ

-ϵr

Minimization of Zone Violations + Energy Minimization (Hybrid)

ℓ(δk , uk) = qδδk + quu2k

s.t. r − ǫ− sk ≤ Cxk ≤ r + ǫ+ sk(sk > 0) =⇒ (δk = 1)

+ϵ

-ϵr

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 10 / 18

Presentation Outline

1 Building Modeling and Control

2 Model Predictive Control

3 Case Study

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 11 / 18

Simulation Study

Closed Loop SimulationParameters

Prediction horizon:N = 10

Sampling time:Ts = 444 sec

Simulation time:Tsim = 31 days

Initial indoor temperature:x4 = 10

◦C

No weather predictions

Evolution of ExternalTemperature

0 5 10 15 20 25 300

5

10

15

20

Time [days]

Tem

pera

ture

[◦C

]

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 12 / 18

Simulation Study

Closed Loop SimulationParameters

Prediction horizon:N = 10

Sampling time:Ts = 444 sec

Simulation time:Tsim = 31 days

Initial indoor temperature:x4 = 10

◦C

No weather predictions

Evolution of ExternalTemperature

0 5 10 15 20 25 300

5

10

15

20

Time [days]

Tem

pera

ture

[◦C

]

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 12 / 18

PI Controller

0 5 10 15 20 25 3014

15

16

17

18

19

20

21

22

referenceinternal temperaturecomfort boundaries

Time [days]

Tem

pera

ture

[◦C

]

0 5 10 15 20 25 30−1000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

PI

Time [days]Energ

yflow

[W]

Control strategy PI

Thermal comfort [%] 87.5Energy consumption [kWh] 753.0Energy savings [%] -

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 13 / 18

Reference Tracking (Basic)

0 5 10 15 20 25 3014

15

16

17

18

19

20

21

22

referenceinternal temperaturecomfort boundaries

Time [days]

Tem

pera

ture

[◦C

]

0 5 10 15 20 25 30−1000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

PIBasic

Time [days]Energ

yflow

[W]

Control strategy PI Basic

Thermal comfort [%] 87.5 89.2Energy consumption [kWh] 753.0 722.7Energy savings [%] - 4.0

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 14 / 18

Comfort Zone Tracking (CZT)

0 5 10 15 20 25 3014

15

16

17

18

19

20

21

22

referenceinternal temperaturecomfort boundaries

Time [days]

Tem

pera

ture

[◦C

]

0 5 10 15 20 25 30−1000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

PICZT

Time [days]Energ

yflow

[W]

Control strategy PI Basic CZT

Thermal comfort [%] 87.5 89.2 84.1Energy consumption [kWh] 753.0 722.7 684.0Energy savings [%] - 4.0 9.1

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 15 / 18

Minimization of Comfort Zone Violations (Hybrid)

0 5 10 15 20 25 3014

15

16

17

18

19

20

21

22

referenceinternal temperaturecomfort boundaries

Time [days]

Tem

pera

ture

[◦C

]

0 5 10 15 20 25 30−1000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

PIHybrid

Time [days]Energ

yflow

[W]

Control strategy PI Basic CZT Hybrid

Thermal comfort [%] 87.5 89.2 84.1 88.2Energy consumption [kWh] 753.0 722.7 684.0 640.1Energy savings [%] - 4.0 9.1 15.0

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 16 / 18

Conclusions - Comfort vs Energy Consumption

84 85 86 87 88 89 90

640

660

680

700

720

740

760

Thermal comfort [%]

Ene

rgy

cons

umpt

ion

[kW

h]

PI

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 17 / 18

Conclusions - Comfort vs Energy Consumption

84 85 86 87 88 89 90

640

660

680

700

720

740

760

Thermal comfort [%]

Ene

rgy

cons

umpt

ion

[kW

h]

PIBasic

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 17 / 18

Conclusions - Comfort vs Energy Consumption

84 85 86 87 88 89 90

640

660

680

700

720

740

760

Thermal comfort [%]

Ene

rgy

cons

umpt

ion

[kW

h]

PIBasicCZT

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 17 / 18

Conclusions - Comfort vs Energy Consumption

84 85 86 87 88 89 90

640

660

680

700

720

740

760

Thermal comfort [%]

Ene

rgy

cons

umpt

ion

[kW

h]

PIBasicCZTHybrid

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 17 / 18

Conclusions

Design and evaluation of different MPC strategies

Best case: hybrid formulation with energy savings up to 15%

Drawback: higher computational complexity

No weather predictions

Suitable for application in ”intelligent“ building

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 18 / 18

Conclusions

Design and evaluation of different MPC strategies

Best case: hybrid formulation with energy savings up to 15%

Drawback: higher computational complexity

No weather predictions

Suitable for application in ”intelligent“ building

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 18 / 18

Conclusions

Design and evaluation of different MPC strategies

Best case: hybrid formulation with energy savings up to 15%

Drawback: higher computational complexity

No weather predictions

Suitable for application in ”intelligent“ building

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 18 / 18

Conclusions

Design and evaluation of different MPC strategies

Best case: hybrid formulation with energy savings up to 15%

Drawback: higher computational complexity

No weather predictions

Suitable for application in ”intelligent“ building

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 18 / 18

Conclusions

Design and evaluation of different MPC strategies

Best case: hybrid formulation with energy savings up to 15%

Drawback: higher computational complexity

No weather predictions

Suitable for application in ”intelligent“ building

J. Drgoňa (STU Bratislava) Building MPC June 20, 2013 18 / 18

Building Modeling and ControlModel Predictive ControlCase Study