Smart buildings aim to reduce energy consumption and peak demand through advanced controls and monitoring occupancy. Technologies like smart meters, sensors, and automated systems allow more efficient operation based on presence and pricing signals. Case studies show potential for demand response through load shifting and peak shaving. However, measuring efficiency solely based on area or consumption can miss factors like occupation density and operating hours. Advanced metrics accounting for these issues may better indicate actual performance.

1. Smart Buildings:
Älykkäät rakennukset tulossa
(Smart buildings are coming)
Aalto Pro 11.6.2012

2. Smart Grid:
For Energy Generators

3. Electricity Generation
Traditional Electricity Grid
 Customers consume electricity and electricity companies
generate electricity to match the demand
© Fortum

4. Electricity Generation
Smart Grid
 Electricity companies shall try to influence WHEN and HOW MUCH
consumers use electricity
© Fortum

5. Electricity Generation
3 Important Elements
 Electricity generators will
operate more efficiently
 Energy consumers will have a
new method of pricing
 Private energy generators can
sell back to the grid more
easily
© Fortum

6. Definitions
Smart grid:
• Energy supply network that shall charge consumers at a variable
energy price per hour
• Prices shall be varied with demand
• First project in Finland January 2013 (Fortum)
• Kalasatamankeskus first smart grid neighbourhood (Helsingin Energia)
Smart meters:
• Electronic energy meters that record detailed customer data
• the amount of energy consumed and when this energy is
consumed
• Information can be viewed in real-time

7. Electricity Generation: Inefficient Load
American example of electricty generation for 1 day
© Data from NIST
1,2
1
Normalized electric system load
0,8
0,6
0,4
0,2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

8. Electricity Generation: Inefficient Load
Finnish example of electricty generation for 1 year (2011)
©Energiateollisuus

9. Finland: Peak electricity
demand in winter

10. Electricity Generation
Finnish peak day energy generation: February 2011
40
35
30
% of peak load
25
20
18%
15
10
5
0
Nuclear Power Hydro Power Wind CHP Condensing Nett imports
Energy generation method

11. Electricity Generation
Cost of generation (approximate costs, operation only)
System €/ MWh
Wind 5
Nuclear Power 15
Hydro Power 20
Condensing Coal 25
CHP 38
Nett imports 49
Conventional gas turbine 125

12. Energy Generation
Prices: Electricity is important
Electricity District Heating
2011 84,4 €/MWh 2011 63,9 €/MWh
2020 110,7 €/MWh1 2020 65,8 €/MWh
Change 31,2 % Change 2.9%
1The Finnish electricity price for 2020 has assumed to be equal to the German
electricity price for 2011. The German 2011 price has been taken from a Eurostat
report that showed German energy prices for mid-size industrial companies (500–
2000 MWh)

13. Smart Grid:
For Energy Consumers

14. Smart Grid: How to reduce consumption
Energy Reduction Load shifting Peak Shaving
Energy Energy Energy
Time Time Time
Options: Options: Options:
• Renewable energy • Smart appliances • React to energy prices by
turning systems on or off
• Energy reduction • Task scheduling
measures • Reduce internal conditions
• Advanced presence
detection

15. ©Zumawire
Smart Grid
Load shifting
 Electric car charging at night
 Smart appliances: dishwashing machine, clothes washing machine
Peak shaving
 Winter peak: turn off night-time lighting or to dim advertisement
lighting when prices are particularly high
 Summer peak: less cooling, target temperature rises from 21oC to
24oC
High supply
 Plenty of wind, take advantage of low energy prices:
 industrial processes that require large amounts of electricity may
be automatically performed
 cheaper to use expensive home systems such as sauna

16. “the wind is blowing in Denmark so maybe we will have a sauna”

17. Smart Buildings:
Case Studies

18. Smart Buildings
© VTT © VTT
VTT test apartment in Oulu
 Opened 2012
 Electric car
 Electricity storage
© VTT
 5.5 kW wind power plant
 20 m2 of solar cells generating 4 kW
 Graphical displays to monitor the electricity
consumption

19. Smart Buildings
Airut, Jätkäsaari, Helsinki
 To open 2015
 Solar power, geothermal heating
 Dashboard:
© Sitra
 smart appliances
 showing energy consumption
 comparing energy consumption with the building average
 booking system for shared cars
 booking system for community sauna
 public transport timetables
© Sitra

20. © Sitra

21. Airut, Jätkäsaari
© Sitra

22. Airut, Jätkäsaari
© Sitra

23. Airut, Jätkäsaari
© Sitra

24. © Sitra
Airut, Jätkäsaari
Heating system: Locking system:
- Radiators with remote controlers - Turns energy systems off
- Pay the heating you use not per m2 - Heating reduced
- Non-essential circuit off
- Lighting off
- Kitchen stove off
- Sauna off
- Ventilation off
Additional metering: Information and control dashboard
- Electrical (per circuit) - Laptop / ipad / phone
- Heating (space / water) - Link to smart software
- Feedback from meters
- Control heating and ventilation
Showers:
- Link to community information
- Water meter per apartment
- Pay the heating you use not per m2
Low2No Smart Systems Selections

25. Smart Buildings
San Francisco Public Utility Commission (SFPUC)
 Opened 2011
 26 000 m2
 450 dashboards providing all building users
with:
• energy consumption
• water consumption
• carbon footprint © SFPUC
©Smart Buildings, LLC

26. Smart Buildings
NASA Ames Research Center
 Opened 2012
 4 750 m2
 5 000 wireless sensors:
 temperature
© io9.com
 carbon dioxide levels
 natural lighting
 air flow
 Construction costs were only 6% more than a traditional
building
 also includes solar panels and geothermal cooling

27. Smart Buildings
Bridesburg Metalworks, Pennsylvania
• Operate 0700 – 1500
• US electricity peak is in summer
• The are paid to turn off metal melting machines
• Melting employees move to the packaging department
• They are earning an extra $25 000 per year
© opower

28. Smart Buildings
New Ways of Working

29. Commercial Buildings:
Peak shaving
• Can we find items to turn off in the middle of the day
• Office staff on holidays, sick, external meetings, sales team
• UK study shows offices on average 45% occupied 2
2: Regus, “Measuring the benefits of agility at work”, May 2011
Energy
• Turn off (where people are missing)
• Lighting
• Ventilation
• Computers

30. Monitoring occupancy
Access control system - measures when people are in the building
Employees log in/out of the building via:
• Electronic time clock
• Smart phone
• Personal computer
• Real time location tags
Building
Location is defined as a set of routines:
OUT IN
• Routine 1: out of the building
• Routine 2: in the building

31. Advanced presence detection
Use presence knowledge to control energy consuming systems
Define location as a set of routines:
• Routine 1: out of the building
• Routine 2: in the building
• Subroutine A: at workspace
• Subroutine B: in a meeting
• Subroutine C: at lunch
Building
OUT IN
OUT IN
OUT IN

32. Use presence to control
Use presence knowledge to control:
• Shut down an individual’s workspace if they leave the building
• Set to standby an individual’s workspace if they are in a meeting / at lunch
• Shut down a lighting / ventilation zone if all of the occupants are out of the office
Example zone control modes:
System Type Presence
Detected
Desk Lighting ON
Common Lighting ON
Equipment ON
Ventilation 100 %
Heating 21oC
Cooling 25oC

33. Use presence to control
Use presence knowledge to control:
• Shut down an individual’s workspace if they leave the building
• Set to standby an individual’s workspace if they are in a meeting / at lunch
• Shut down a lighting / ventilation zone if all of the occupants are out of the office
Example zone control modes:
System Type Presence No presence
Detected 15 mins
Desk Lighting ON OFF
Common Lighting ON ON
Equipment ON STAND BY
Ventilation 100 % 100 %
Heating 21oC 21oC
Cooling 25oC 25oC

34. Use presence to control
Use presence knowledge to control:
• Shut down an individual’s workspace if they leave the building
• Set to standby an individual’s workspace if they are in a meeting / at lunch
• Shut down a lighting / ventilation zone if all of the occupants are out of the office
Example zone control modes:
System Type Presence No presence No presence
Detected 15 mins 1 hour
Desk Lighting ON OFF OFF
Common Lighting ON ON OFF
Equipment ON STAND BY STAND BY
Ventilation 100 % 100 % 50 %
Heating 21oC 21oC 20oC
Cooling 25oC 25oC 27oC

35. Use presence to control
Use presence knowledge to control:
• Shut down an individual’s workspace if they leave the building
• Set to standby an individual’s workspace if they are in a meeting / at lunch
• Shut down a lighting / ventilation zone if all of the occupants are out of the office
Example zone control modes:
System Type Presence No presence No presence No presence 2
Detected 15 mins 1 hour hours
Desk Lighting ON OFF OFF OFF
Common Lighting ON ON OFF OFF
Equipment ON STAND BY STAND BY OFF
Ventilation 100 % 100 % 50 % Night time mode
Heating 21oC 21oC 20oC Night time mode
Cooling 25oC 25oC 27oC Night time mode

36. Concept development
Virtual Energy
Smart Grid
Prices
Advanced
Advanced Presence
Controls Detection
Technology

37. Incentive schemes
Residential building example:
• A block of similar 2 bedroom apartments
• Incentive scheme to reduce energy
• Reward given to the lowest energy consumption

38. Occupation density
Energy directly related to people is not considered by area metrics
Average occupation density in UK offices is 11.8m2 per workspace3
• 77% of workspaces between 8m2 & 13m2 per workspace
• Using kWh/m2, 13m2 per workspace will seem more energy
efficient than 8m2 per workspace
3: Occupier Density Study Summary Report, British Council for Offices, June 2009
Source: Fooducate.com

39. Case study: Occupation density
Simulated case study: office building in Helsinki
• Area: 4650m2
• Hours of occupancy 08:00 – 17:00 (9 hours)
Similar day lengths, different occupation densities
Case A B C
Population density (m2/person) 8 10 12
Number of occupants 500 400 332
Energy consumption (kWh/m2) 102 99 98
Energy consumption (kWh/person) 951 1150 1368
Energy consumption (Wh/m2h) 0.087 0.105 0.126
Results
• kWh/m2: case C consumes the least
• kWh/person or Wh/m2h: case A consumes the least
(C consumes 44% more than A)

40. Hours of occupation
Not considered by area metrics
• Comparison of two similar healthcare buildings
• Hospital ”A” open 24 hrs / Hospital ”B” open 12 hrs
• kWh/m2 does not provide an allowance for the longer day of ”A”
• Thus ”A” has a higher energy consumption per m2 and seems
less energy efficient

41. Case study: Hours of occupation
• Simulated case study: office building in Helsinki
• Area: 4650m2
• Population density 10m2 / person
Similar occupation densities, different day lengths
Case D E F
Working hours per day (h) 12 9 6
Hours of occupancy 08 - 20 08 - 17 09 - 15
Energy consumption (kWh/m2) 115 99 84
Energy consumption (kWh/person) 1330 1150 981
Energy consumption (Wh/m2h) 0.092 0.105 0.134
Results
• kWh/m2: case F consumes the least
• kWh/person or Wh/m2h: case D consumes the least
(F consumes 45% more than D)

42. Concept development
Virtual Energy
Smart Grid
Prices
Advanced
Advanced Presence
Controls Detection
Technology
Measure per Measure
Person Wh/m2h
Behaviour Motivation /
Change Incentives

43. Summary
• Smart grid will bring more efficiency in energy generation
• Cheaper prices on average, but a different way of charging
• People who prepare for smart grid will save money – people who dont
prepare will pay more
• Can we reduce our peak load AND measure energy efficiency more accurately?

44. Ken Dooley
Sustainability Group Manager
Energy and Environment
ken.dooley@granlund.fi