use of weather and occupancy forecasts for optimal ... · opticontrol project presentation version...

Use of Weather and Occupancy Forecasts for Optimal Building Climate Control

(OptiControl)

Project Presentation

The OptiControl Team http://www.opticontrol.ethz.ch/

Version 15. Jan. 2009

OptiControl Project Presentation Version 15. Jan. 2009

2

OptiControl – Overview Aims:

Development of methods to exploit weather forecasts and occupancy-related information aiming at

  improving the energy efficiency and comfort of buildings;

  reducing peak electricity demand.

Expected Results:

•  Methods

•  Software/tools

•  Benefit-cost analyses

•  Application to demonstrator


3

OptiControl – Approach

•  Case studies (Selected “Applications”, typical buildings, representative sites etc.)

•  Extensive use of computer-based modeling & simulation (Controller development, potential assessment, tests etc.)

•  Emphasis on Model Predictive Control (MPC)

•  Stepwise refinement & simplification of methods and models

•  Field tests of the new control approaches in demonstrator object(s)


4

OptiControl – Research Partners

•  ETH Systems Ecology Group Modeling and simulation; project management

•  ETH Institut für Automatik Control theory; controller development & tools

•  EMPA Building Technologies Laboratory Building physics, HVAC & energy systems, modeling, field tests

•  MeteoSwiss Meteorological data and predictions (Europe–local)

•  Siemens Building Technologies BAC research and product development


5

OptiControl “Applications”

1.  Integrated Room Automation (IRA)

Integrated automation of light, blind, heating, cooling and ventilation

(including TABS and floor heating subsystem variants)

In preparation: 2.  Cooling by night-time ventilation 3.  Energy Recovery 4.  Generic Energy Flux Control


6

Case Study Sites

Zürich Basel-Binningen Genève-Cointrin Lugano Modena Marseille-Marignane Clermont-Ferrand Mannheim Hohenpeissenberg Wien Hohe Warte

x

x

x x

x

x x x x

x

x


7

Buildings Definitions

Controllers Library

Weather DB

Occupancy Datasets

Weather Pred. DB

Occupancy Predictions

Simulation results

Modeling & Simulation Environment

Controller model

“Real” building model

Controller

Modeling

Building specif.

& params

Location specif.

& data

Controller specif.

& params

Occup. specif.

& data

Predicted weather

data

Predicted occup. data

MoEDSiPA Statistics,

Perf. Indices, Graphics etc.

Exper. Definition Simulation Post Analysis


8

0 20 40 60 80 100 120

Ener

gy /

disc

omfo

rt c

osts

Controller Assessment Information Levels: 1. “perfect world – we know everything” 2. “real world, no weather forecasts” 3. “real world, with weather forecasts”

Improvement of present-day control strategies

Transition from perfect models to real world

realistic

Potential

(theoretical)

Reference (today’s practice)

Improved non- predictive control

Predictive control

Performance Bound


9

8 building zone types:

Sample Results: Definition of Simulation Experiments

Façade orientation Southwest Thermal insulation level Swiss average Passive house Construction type Heavyweight Lightweight Window area fraction 30% 80 % Internal gains level low high

HVAC System (OptiControl System #01): •  Blinds •  Electric lighting •  Heating: radiators •  Cooling: slow ceiling

– mechanical chiller – free cooling with wet tower


10

Sample Results: Control Strategies Considered

•  Ref2 State-of-the-art rule based control

•  Ref3 Improved rule based control (new) •  MPC-CE MPC-Certainty Equivalent control *)

•  PB Performance Bound

n = Narrow thermal comfort range

w = Wide thermal comfort range

*) Using “COSMO-7” weather forecasts by MeteoSwiss, preliminary results.


11

(Ref2n - Ref3n)/Ref2n

-10%0%

10%20%30%40%50%60%70%

0 100 200 300

Ref2n [kWh/m2/a]

Ref2n - Ref3n

-200

20406080

100120140

0 100 200 300

Ref2n [kWh/m2/a]

[kW

h/m

2/a

]

Results (1) – Improved Non-Predictive Control


12

Results (2) – Potential of Weather Forecasts

Ref3w - PBw

0

2

4

6

8

10

0 50 100 150 200

Ref3w [kWh/m2/a]

[kW

h/m

2/a

]

(Ref3w - PBw)/Ref3w

0%

10%

20%

30%

40%

0 50 100 150 200

Ref3w [kWh/m2/a]


0

10

20

30

40

50

60

Ref2 Ref3 MPC-CE PB

[kWh/m2/a]

0

20

40

60

80

100

Ref2 Ref3 MPC-CE PB

[kWh/m2/a]

13

Results (3) – Comparison of Control Strategies

0

5

10

15

20

25

30

Ref2 Ref3 MPC-CE PB

[kWh/m2/a]

0

5

10

15

20

25

30

Ref2 Ref3 MPC-CE PB

[kWh/m2/a]

West / Wien / Swiss average / heavy / windows 30%

South / Zurich / Swiss average / heavy / windows 30%

South / Marseille / Swiss average / heavy / windows 30%

South / Zurich / Passive House / Light / windows 80%


14

•  First results are promising

•  Benefit of weather predictions varies strongly from case to case

•  Appropriate tools are important

•  Our sophisticated studies can be useful for identifying simple, improved control strategies

Conclusions

use of weather and occupancy forecasts for optimal ... · opticontrol project presentation version...

Documents