State of the Art in Building Automation and Technology

State of the Art in Building Automation and Technology Emerging Trends and RDI Challenges

Gerhard Zucker AIT Austrian Institute of Technology Energy Department

European Construction Technology Platform www.ectp.org

Energy Efficient Buildings Association www.e2b-ei.eu

Content   

Energy Efficient Building Operation Photovoltaics in Buildings Smart Buildings in a Smart City

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Building Automation: Building Services Domain Climate Control Visual Comfort Personal Safety Building Security Transportation One-way Audio Energy Management Supply and disposal Communication and information exchange Other special domains

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Typical Building Services HVAC, Humidity, Air Quality Artificial lighting, daylighting (motorized blinds/shutters), constant light control Fire alarm, gas alarm, emergency sound system, emergency lighting, CCTV (closed circuit television) Intrusion alarm, access control, water leak detection, CCTV, audio surveillance Elevators, escalators, conveyor belts Public address/audio distribution and sound reinforcement systems Energy efficiency, peak avoidance, integration of renewable energy sources (RES) Power distribution, waste management, fresh water/domestic hot water, waste water IT Networks, PBX (Private Branch Exchange), Intercom, shared WAN access, wireless access (WLAN) Clock systems, flextime systems, presentation equipment (e.g., video walls), medical gas, pneumatic structure support systems (for airhouses) 3

Introduction Share of total EU Energy Consumption

Share of Buildings Energy Consumption

HVAC Lighting Elevators Other

http://www.glassforeurope.com/en/issues/faq.php 26.06.2014

http://sourcingelectricals.net/cfls?page=6 4

Energy Efficient Building Operation

Energy Efficient Building Operation 



Building management system collects data  Byproduct from regular operation  Current data is used by facility manager online  Historic data is archived Efficiency is defined by few performance indicators  Cumulated annual consumption  Specific heating demand / electric consumption 105 90 75

kW

60 45 30 15 0 01.10.09

03.10.09

05.10.09

07.10.09

09.10.09

11.10.09

13.10.09

Total electric consumption of a building (2 weeks) 26.06.2014

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Low Hanging Fruit: Basic Analysis of Historical Data 80

Annual consumption sorted by industries HVAC

Lighting

Elevators

Other

75 70 65 60 55 45

38,2 %

40 35

29% 42%

Source: Georg Benke, e7

kW

50

27,9 %

30 25 20 15

8%

21%

10

Base load

5

Grundlastbereich

0 0

Annual summary of  Indoor temperature over weekday (weekend vs workday)  Indoor temperature over time of day (nightly setback)  Heating demand over daily temperature 26.06.2014

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

Zeit (Stunden)

Sorted load profile (electric, 1 year)

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Statistical Deviation 105 90 75

kW

60 45 30 15 0 01.10.09

03.10.09

05.10.09

07.10.09

09.10.09

11.10.09

13.10.09

Expected Temperature

Actual Temperature

Box Plot Visualization

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Visualizations

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Recurrence Plots Heat maps

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Error identification and diagnosis

Clustering    

Temperature distribution in heating system Using historic data Clustering creates cold-state and hot-state No configuration needed (e.g. hot = 25° to 30°)

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Clustering Example: Heat Rejection 

 

2 parameters for clustering  Temperature difference (inlet minus outlet) over mass flow Blue clusters: normal operation Red cluster: low mass flow with low temperature drop 11

Operation-Cluster

10

9

T

8

Error-Cluster

7

Operation-Cluster

6

5

4 10

12

14

16

18

20

22

24

m³/h 26.06.2014

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Continuous Optimization of Operation

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Continuous Optimization of Operation Model-Based Control  Models of  Energy systems (PV, heat pump, HVAC, …)  Building (walls, materials, window areas, …)  Input data  Building monitoring data  Forecasts for weather, occupancy, irradiation

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Continuous Optimization of Operation Model-Based Control  Goal  „Maximize on-site usage of PV“ or  „Minimze costs“  Constraints  „Do not violate indoor comfort“  „Do not switch heat pump more than twice a day“  Optimizer finds best optimization strategy for the next few hours

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Benefits of Model Based Control   

Optimize a whole day in advance Align consumption with production  E.g. run heat pump when PV is available Define goals instead of setpoints

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Photovoltaics in Buildings

Photovoltaics in Buildings 





Cities aiming to meet the 20-20-20 goals  Requires more renewables in the city Consequence  Demand shall follow production  PV in refurbishment Demand side management  Use thermal storages of buildings  Electric storage: small batteries  Peak shaving with small battery to support grid  Inverters can provide reactive power, change phase-angle phi to stabilize electric grid

Implications of On-site Consumption in the City   

PV: east/west oriented instead of south Refurbishment: PV in facade PV as sunblinds / anti-glare  Building automation topic  PV becomes an add-on to sunblinds  Shading reduces cooling costs -> Total Cost Ownership

PV on South-East Facade

1,5 kWp 26.06.2014

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International Activities  IEA-SHC Task 41: Best practice examples for solar architecture, evaluation of planning tools  IEA-SHC Task 53: New Generation Solar Cooling & Heating Systems

 IEA-PVPS Task 15: BIPV (starting)  EERA: EUROPEAN ENERGY RESEARCH ALLIANCE, SUB-PROGRAMME 5: PV Systems

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Smart Building in a Smart City System in a System

Buildings in a City Context Optimisation of the overall system of a buidling => Optimisation of the overall system of a city

Building

District

City

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Smart Energy Infrastructure Optimisation of the overall system  Integration of the infrastructure (electrical, thermal, ICT) in design and operation  Load shifting => ‚Building to Grid‘  Interaction + communication of the networks with the consumer and producer  Real-time optimisation of the overall system  Multi-criteria optimisation  Smart Consumers 26

Smart Buildings in a Smart City  

   



Building: complex system Traditionally  supervisory control level  process level  field level Nothing above supervisory control Change: building is one component in the infrastructure of a Smart City Technological level: communication protocols Organisational level: who is capable to do what?  Understanding building processes  Maintenance of electric grid  Pooling of storage & production Building as a service provider

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Demand Side Management: Load Shifting Event

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5 Normal Operation Load Shifting

Actual Load Shift/MW

Electric Consumption/MW

20

16 14 12 10 8 6 4

0

-5

2 0 0

7

9

12

time/h

24

-10 0

7

9

12

24

time/h

Simulation of 860 dwelling with direct electric heating, built between 1981 and 1990. Load shifting between 7:00 and 9:00. The right figure shows the difference between normal operation and load shift. 26.06.2014

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What research is doing in that respect… EERA Joint Programme Smart Cities

~80 Research organisations and institutes Lead by AIT     

Sub-Programme 1 Energy in Cities Sub-Programme 2 Urban Energy Networks Sub-Programme 3 Energy-efficient Interactive Buildings Sub-Programme 4 Urban City-related Supply Technologies Sub-Programme 5 PV Systems

Coordination of research tasks and acitivties under within the topic of Smart Cities 29

AIT Austrian Institute of Technology

Gerhard Zucker AIT Austrian Institute of Technology Energy Department