Welcome

The Building Automation and Control Tool (BACTool) allows you to assess the performance of selected algorithms for the control of buildings.

In particular BACTool allows you to explore the potential of predictive building control.

Start with the Buildings Selection to select two out of a total of 2'304 annual cases currently supported. All cases represent office buildings.

Then step through the Performance Analysis to analyze and compare energy usage, occupant comfort, and peak power demand.

Documentation on the underlying data and methods is available under Further Info and from the web site of the OptiControl project.

Initiated by

LogoETH

LogoSiemens

LogoGruner

LogoEMPANew

LogoBund

LogoMCH

Supported by

LogoSER

LogoCCEM

Type of Investigation

 Theoretical potential of predictive control for a building zone

 Comparison of rule-based control algorithms for a building zone

 Custom

Building

Typical Swiss office building at site Zurich
heavy_office Facade orientation
HVAC system variant and automated subsystems North South
S1: Blinds, lighting, cooled ceiling, free cooling, radiators
S2: Same as S1 + mech. ventilation with energy recovery
S3: Blinds, lighting, mech. ventilation with energy recovery
Very well insulated, highly glazed office building at site Marseille
heavy_office Facade orientation
HVAC system variant and automated subsystems North South
S1: Blinds, lighting, cooled ceiling, free cooling, radiators
S2: Same as S1 + mech. ventilation with energy recovery
S3: Blinds, lighting, mech. ventilation with energy recovery
Please use Building A and Building B in the main menu.

pointer Control algorithm

 PBPerformance Bound (perfect control based on Model Predictive Control)
 RBC-ARule-Based Control, variant A
 RBC-BRule-Based Control, variant B

Further info...

pointer HVAC system

 S1Blinds, lighting, cooled ceiling, free cooling, and radiators
 S2Same as S1 plus mechanical ventilation with energy recovery
 S3Blinds, lighting, and mechanical ventilation with energy recovery

pointer Site

 ZurichCentral European climate with frequent temperature inversions/foggy conditions during autumn and winter
 ViennaTransition from oceanic climate to humid continental climate with cold winters and warm summers
 LuganoModerate annual temperature variation, sunny winter climate, heavy rainfalls during autumn and spring
 MarseilleMild, dry winters and hot, sunny summers mitigated by a sea breeze

Further info...

pointer Facade orientation

 NorthBuilding zone with North facing facade
 SouthBuilding zone with South facing facade
 South+EastCorner zone with South plus East facing facade
 South+WestCorner zone with South plus West facing facade

pointer Construction type

 Heavyweightcdyn ≈ 80 Wh/m2K
 Lightweightcdyn ≈ 36 Wh/m2K

where cdyn denotes the internal dynamic heat capacity of the building zone.

pointer Building standard

 Swiss averageUop ≈ 0.6 W/m2K,   Uwin ≈ 2.8 W/m2K
 Passive houseUop ≈ 0.1 W/m2K,   Uwin ≈ 0.7 W/m2K

where Uop and Uwin denote the overall heat transfer coefficients of opaque facade parts and of windows including frames, respectively.

pointer Window area fraction

 Low30% window area per facade
 High80% window area per facade

pointer Internal heat gains level

 LowOccupants gains 5 W/m2/person, equipment gains 7 W/m2
 HighOccupancy gains 9 W/m2/person, equipment gains 15 W/m2

Further info...

pointer Thermal comfort

  Wide, outside air temperature dependent

Further info...

pointer Indoor air quality

  Non-air quality controlled ventilation

Mechanical ventilation – if present – was operated using a fix schedule, with margins of 1h before/after begin of work.

pointer Illuminance comfort

  Occupancy dependent, bright

Applied was a lower illuminance setpoint value for occupied offices of 500 lux. No upper limit was defined, assuming that in case of excess incoming solar radiation the user would be able to obtain glare protection by manual adjustment of an internal blind.

pointer Energy system

  Heat: earth coupled heat pump; Cold: mechanical (compression) chiller

pointer Control costs

  Primary energy

pointer Building type

  Office building

pointer Weather data

  DRY (Design Reference Year)

pointer Control algorithm

 PBPerformance Bound (perfect control based on Model Predictive Control)
 RBC-ARule-Based Control, variant A
 RBC-BRule-Based Control, variant B

Further info...

pointer HVAC system

 S1Blinds, lighting, cooled ceiling, free cooling, and radiators
 S2Same as S1 plus mechanical ventilation with energy recovery
 S3Blinds, lighting, and mechanical ventilation with energy recovery

pointer Site

 ZurichCentral European climate with frequent temperature inversions/foggy conditions during autumn and winter
 ViennaTransition from oceanic climate to humid continental climate with cold winters and warm summers
 LuganoModerate annual temperature variation, sunny winter climate, heavy rainfalls during autumn and spring
 MarseilleMild, dry winters and hot, sunny summers mitigated by a sea breeze

Further info...

pointer Facade orientation

 NorthBuilding zone with North facing facade
 SouthBuilding zone with South facing facade
 South+EastCorner zone with South plus East facing facade
 South+WestCorner zone with South plus West facing facade

pointer Construction type

 Heavyweightcdyn ≈ 80 Wh/m2K
 Lightweightcdyn ≈ 36 Wh/m2K

where cdyn denotes the internal dynamic heat capacity of the building zone.

pointer Building standard

 Swiss averageUop ≈ 0.6 W/m2K,   Uwin ≈ 2.8 W/m2K
 Passive houseUop ≈ 0.1 W/m2K,   Uwin ≈ 0.7 W/m2K

where Uop and Uwin denote the overall heat transfer coefficients of opaque facade parts and of windows including frames, respectively.

pointer Window area fraction

 Low30% window area per facade
 High80% window area per facade

pointer Internal heat gains level

 LowOccupants gains 5 W/m2/person, equipment gains 7 W/m2
 HighOccupancy gains 9 W/m2/person, equipment gains 15 W/m2

Further info...

pointer Thermal comfort

  Wide, outside air temperature dependent

Further info...

pointer Indoor air quality

  Non-air quality controlled ventilation

Mechanical ventilation – if present – was operated using a fix schedule, with margins of 1h before/after begin of work.

pointer Illuminance comfort

  Occupancy dependent, bright

Applied was a lower illuminance setpoint value for occupied offices of 500 lux. No upper limit was defined, assuming that in case of excess incoming solar radiation the user would be able to obtain glare protection by manual adjustment of an internal blind.

pointer Energy system

  Heat: earth coupled heat pump; Cold: mechanical (compression) chiller

pointer Control costs

  Primary energy

pointer Building type

  Office building

pointer Weather data

  DRY (Design Reference Year)

Building A
Building B
Total energy
Heating
Cooling
Lighting
Ventilation
Electric lighting
Fan operation
Radiator heating
Heating mech. ventilation
Cooling mech. ventilation
Cooled ceiling, chiller
Cooled ceiling, free cooling
Building A
Building B


Building A
Building B






Building A
Building B
Building A
Building B

Background

BACTool focuses on the exploration of so-called "non-standardized" control solutions, i.e. solutions where the control is being tailored to the given building, combination of automated subsystems and user requirements by means of corresponding programs that govern the behavior and interplay of the individual subsystems. Building automation systems with programmable controllers are typically used for that purpose. Traditional programs are susbsumized under the term Rule-Based Control (RBC). A promissing alternative that is currently undergoing intensive research is co-called Model Predictive Control (MPC). A brief introduction to RBC and MPC is given below. A more in-depth discussion of the two approaches and a comprehensive list of criteria for the evaluation of non-standardized control solutions is given in [1] (in particular see the Introduction therein).

All data presented in the BACTool are simulation results. They were produced using the data and methods described in [1]. The simulations were based on a 12th order multiple-input-multiple-output bilinear model of the coupled thermal, light and air quality dynamics of a single room or building zone. The model is used, firstly, as a "plant model" to simulate the building zone's response to different control algorithms. Secondly, the same model is also used as a "controller model" for Model Predictive Control (see below). Details on the model can be found in [2] and [5]. Each simulation covered one year and employed a time step of one hour. The used weather data sets are documented in [6]. Statistical analyses dealing with trends and patterns across a large number of simulations are provided in [7],[8],[9],[10]. A more detailed analysis of selected cases can be found in [8].

Control Algorithms

Rule-Based Control (RBC) determines the control inputs based on a series of rules of the kind "if condition then action". The conditions and actions typically involve numerical parameters (e.g., threshold values), the so-called control parameters. In BACTool they are determined from building parameters based on carefully derived, automated calculation procedures. The used RBC algorithms consist of a high-level and a low-level part. The high-level part yields operating modes that determine the "low-cost" actions (blind positioning, free cooling operation, and energy recovery operation). The low-level part determines, firstly, the control actions for the "low-cost" action aggregates. In a second step it calculates the remaining control outputs for "high-cost" actions such as active heating or cooling, and mechanical ventilation. The various RBC variants differ only in their high-level parts. For further information see [3]

Model Predictive Control (MPC) relies upon a model of the building that is used together with predictions of relevant disturbances (e.g., weather, internal gains) to predict the system's future evolution. At the beginning of each time step (e.g., every hour) MPC computes the "best possible" sequence of control actions that minimize a cost function (e.g., total energy demand) over a given prediction horizon (e.g., a few days) while respecting comfort (e.g., illumination levels, room temperature range) and any other (e.g., maximum power demand) constraints. The control actions identified for the very first time step within the prediction horizon are then applied to the system, and the whole procedure is repeated at the beginning of the next time step. This "receding horizon" approach ensures that the control plan is continuously updated using the newest information on the building's state, thus allowing to account for model inaccuracies or any unknown disturbances that have meanwhile acted on the building. See also [4].

The Performance Bound (PB) is a theoretical value that presents the lowest achievable control cost (in terms of energy or money) for a given building, cost function, disturbances (weather, internal gains) and set of comfort requirements. The PB can be estimated by applying Model Predictive Cnotrol (see below) over a representative period (e.g., one year) assuming a perfect building model, and perfect knowledge of all future disturbances. Knowledge of the PB makes it possible to compare different design variants for a given system net of any effects related to control.

Theoretical energy savings potential: By definition the PB presents a theoretical number that can not be beaten by any real controller. The difference between a real controller's energy usage and the PB gives a measure of the maximum achievable improvement for that controller. Nothing can be said about to what extent the potential can be exploited by a feasible control. However, the size of the potential indicates for what applications further control strategy development may be promising.

  • PB: The PB calculation in BACTool uses a perfect MPC model and perfectly known disturbances. The optimal sequence of control inputs is therefore determined only once every TOL = 48 h (2 d) using a prediction horizon of TH = 144 h (6 d). The subscript "OL" stands for "open loop" and this refers to the fact that the control inputs during the 48 hours following an optimization are precisely the ones delivered by this optimization, i.e. during these 48 hours the control inputs are directly applied to the plant model without any feedback to the controller. Further information can be found in [7].
  • RBC-A: A typical, broadly applied, non-predictive control strategy. Inputs for control are current measurements of room temperature, outside air temperature, external heat gains, and the occupancy state. Three blind transmission values are considered: fully open, fully closed and shading transmission. Blinds are repositioned depending on threshold crossings for solar gains. For simulation this behavior is approximated by setting the blinds for a given time step based on the time step's average solar gains. This control strategy correponds to the strategy "RBC-1" reported in [3].
  • RBC-B: A novel, non-predictive control strategy. The controller inputs are the same as for RBC-A; in addition are used historical heat and cold demand signals, and historical room temperature data. Blind transmission varies continuously between a minimum (blinds fully closed) and maximum (blinds fully opened) value. Blinds are repositioned once per hour (once per control step), based on the historical signals and data. See control strategy "RBC-4" in [3].

Note, the set of subsystems actually controlled by a given control algorithm depends on the currently chosen HVAC System variant.

Sites

Site Name Latitude Longitude Elevation
[m a.s.l.]
Annual Mean
Temperature
[°C]
Annual Mean
Global Radiation
[W/m2]
Zurich47.4° N8.6° E5569.3125
Vienna48.3° N16.4° E20911.4140
Lugano46.0° N9.0° E27312.7°140
Marseille43.4° N5.2° E515.3°182

Internal Gains

The occupancy density of the building (a number ranging from 0-100%) was used as the key quantity to determine the internal heat gains from persons and equipment, plus CO2 production. Considered were two internal gains levels based on the Swiss standard SIA 2024 [11].

Parameter Unit Internal Gains Level
Low High
Floor area per person m2 14 7.8
Internal gains due to persons W/m2 5 9
Internal gains due to equipment W/m2 7 15
CO2 production m3/h/m2 1.1e-3 1.9e-3

Diurnal and weekly variations in internal gains were obtained from the Swiss standard SIA 2024 [11] for cellular offices. During weekends no persons were assumed to be present and the person gains were set to zero. The equipment gains were set to the weekdays' night-time value.

Thermal Comfort

The used minimum and maximum room temperature set points for heating and cooling were similar to the definitions in SIA 382/1 [12].

ThermalComfortRange

The actual range at a given point in time was determined as a function of the exponentially weighted running mean of the past measured outside air temperature values. The running mean was calculated in a similar manner as described in EN 15251 [13]. The comfort settings were applied 24 hours a day and 7 days a week.

References

  1. Gyalistras, D. & Gwerder, M. (Eds.) (2010). Use of weather and occupancy forecasts for optimal building climate control (OptiControl): Two years progress report. Terrestrial Systems Ecology ETH Zurich, Switzerland and Building Technologies Division, Siemens Switzerland Ltd., Zug, Switzerland, 158 pp, Appendices. ISBN 978-3-909386-37-6. images/pdf_icon_tiny.gif
  2. Lehmann, B., Wirth, K., Dorer, V., Frank, Th. & Gwerder, M. (2010a). Control problem and experimental set-up. In [1], Chapter 2, pp 15–28.
  3. Gwerder, M., Tödtli, J. & Gyalistras, D. (2010). Rule-based control strategies. In [1], Chapter 3, pp 29–42.
  4. Oldewurtel, F., Jones, C.N., Parisio, A. & Morari, M. (2010). Model predictive control strategies. In [1], Chapter 4, pp 43–58.
  5. Lehmann, B., Wirth, K., Carl, S., Dorer, V., Frank, Th. & Gwerder, M. (2010b). Modeling of buildings and building systems. In [1], Chapter 5, pp 59–66.
  6. Stauch, V., Schubiger, F. & Steiner, P. (2010). Local weather forecasts and observations. In [1], Chapter 6, pp 67–78.
  7. Gyalistras, D., Lehmann, B., Wirth, K., Gwerder, M., Oldewurtel, F. & Stauch, V. (2010). Performance bounds and potential assessment. In [1], Chapter 7, pp 79–106.
  8. Gyalistras, D., Wirth, K. & Lehmann, B. (2010). Analysis of savings potentials and peak electricity demand. In [1], Chapter 8, pp 107–134.
  9. Gyalistras, D., Gwerder, M., Oldewurtel, F., Jones, C.N., Morari, M., Lehmann, B., Wirth, K., & Stauch, V. (2010). Analysis of energy savings potentials for Integrated Room Automation. Paper presented at the 10th REHVA World Congress Clima 2010, 9-12 May 2010, Antalya, Turkey, 8pp. images/pdf_icon_tiny.gif
  10. Gwerder, M., Gyalistras, D., Oldewurtel, F., Lehmann, B., Wirth, K., Stauch, V. & Tödtli, J. (2010). Potential assessment of rule-based control for Integrated Room Automation. Paper presented at the 10th REHVA World Congress Clima 2010, 9-12 May 2010, Antalya, Turkey, 8pp. images/pdf_icon_tiny.gif
  11. SIA 2024 (2006). Standard-Nutzungsbedingungen für die Energie- und Gebäudetechnik, Raumnutzungen "Einzel-, Gruppenbüro" und "Grossraumbüro", pp. 34-37.
  12. SIA 382/1 (2007). Lüftungs- und Klimaanlagen – Allgemeine Grundlagen und Anforderungen.
  13. Standard EN 15251 (2007). Eingangsparameter für das Raumklima zur Auslegung und Bewertung der Energieeffizienz von Gebäuden – Raumluftqualität, Temperatur, Licht und Akustik.

Publisher

Automatic Control Laboratory, ETH Zurich

Editor

Dimitrios Gyalistras and the OptiControl Team

Programming

Jan Siroky

System Responsible

IT Support Group, D-ITET/ETH Zurich

Initiated by

LogoETH

LogoSiemens

LogoGruner

LogoEMPANew

LogoBund

LogoMCH

Supported by

LogoSER

LogoCCEM