impact of user behavior on energy consumption in high-performance...

© Fraunhofer USA 2010

Fraunhofer Center for Sustainable Energy Systems

Impact of user behavior on energy consumption in high-performance buildings –Results from two case studies

Kurt Roth, Ph.D.

Peter Engelmann

Denver, COJuly 21, 2010


Monitoring examples: Two student dormitories in Europe

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Dormitory “Neue Burse”, Wuppertal, Germany• Two buildings from 1978• Refurbished 2001 / 2004 to low energy

house and passive house• LEH:303 units, 84,200 ft2• PH: 323 units, 85,970 ft2• Mostly 1-bedroom apartments

Dormitory “Molkereistrasse”, Vienna, Austria• Built in 2005 as passive house• 278 units, 88,420 ft2• Mostly 2-bedroom apartments


Monitoring principles

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on sitedelivery conversion storage distribution handover

FuelHeatChillPowerIrradiation

decreasing availability of measured datadecreacing accuracy of calculated data

aux. energy

aux. energy

aux. energy

lossloss

lossloss

schematic energy flow

roomuser


Detection of Window Opening by reed switch

X-Axis: time in daysY-Axis: time of day

Window closed: whiteWindow open: red

Typical patterns in days (weekday/weekend) and intraday (morning, afternoon).

User behavior example: window openingWindow opening: typical user patterns


• Comparison between apartments shows variety of behavior patterns – but no significant difference between apartments with or without ventilation system.•Adaequate air exanges cannot be ensured by window-ventilation.•Additional air-exchange increases ventilation heat losses.

Design quandry!

User behavior: window openingWindow opening: Students who wish to sleep with windows opened


• Opening windows depends on individual habits and, strongly, on outdoor temperature.

•Without mechanical ventialtion, insufficient air quality because air exchange rates too low.

User behavior: window opening

0%

10%

20%

30%

40%

50%

60%

70%

80%

> 68°F 50 to 68°F 32 to 50°F < 32°F

Percentage window is open mean value all apartments

Window opening: depending on outdoor conditions


Neue Burse Wuppertal, measurement January to March 2005, hourly values.

•Limited influence on space heating in the apartment, central temperature setpoint. •Mean value during heating period: 71.1°F (22.3°C)

User behavior: indoor air temperaturesIndoor air temperatures: exceeding design temperatures


Molkereistraße Vienna, measurement January to March 2007, hourly values.Individual themostat in each room.

• Mean value during heating period: 73.8°F (23.3°C) vs. 68oF (20oC)

User behavior: indoor air temperaturesIndoor air temperatures: exceeding design temperatures


User behavior: consumption patterns and challengesTotal electricity consumption in Wuppertal:• typical pattern for internal gains: high on weekdays, low on weekends/holidays• problematic with central heat recovery: little occupancy (means low internal gains)

in times of low ambient temperatures (example: Christmas)


User behavior: consumption patterns and challengesDomestic hot water usage:• Pattern correlates with occupancy• Due to high occupation density: heat for DHW is dominating load• Challenge for using renewable energy, especially solar thermal


Site energy consumption

55%

Collection of consumption data from other dormitories show an average heat-energy consumption of 163kWh/m2a (incl. DHW).But very different types of buildings and age!

•Compared to the average, PH dormitories decrease heating energy consumption by >50%•DHW and MELs _ Lighting become the dominant loads.•Reduction of electric power consumption: still room for improvement!


Site energy consumption – user influence

Example of “Neue Burse” in Wuppertal shows:• 32% increase in space heating energy consumption compared to energy model • User behavior responsible more than half of increase (18%), mainly due to higher indoor

temperatures (design temperature: 68°F) and additional ventilation heat loss • Problems in operation controls caused ~similar additional energy loss

14%

18%32%


Conclusions from case studies presented

• Clear behavior has a major impact on actual versus expected energy consumption in low-energy buildings

• Cuts across most end uses •In both cases, suboptimal behavior did not prevent building from achieving large energy savings • “Better” behavior would have achieved appreciable energy savings for space heating end use • Can deduce occupant behavior from energy monitoring in some cases

“Asides”• Water heating and MELs: Major portion of total energy consumption in low-energy building • Commissioning can be important for low-energy buildings


Perspectives on gaps and barriers

Key areas for future research1. How occupant behavior impacts design option selections 2. Energy savings opportunities from encouraging energy-saving behaviors

Questions that follow: • How much does occupant behavior affect energy consumption in very low-energy homes in different climates? Range?• How does occupant behavior impact the robustness of different energy efficiency measures in different climates? Range?•What are the most effective approaches to save energy related to occupant behavior?


Research needs

• Evaluation of the value / robustness of different energy efficiency measures as a function of occupant behavior distributions•Detailed monitoring and modeling of occupant behaviors and their energy impacts

• More detailed submetering, monitoring in low-energy homes • Extend monitoring results to general population

•Correlations between different types of behaviors in households? • Evaluations of potential approaches to encourage occupants to save energy -what works and doesn’t for a range of behaviors and households

• Avoiding waste / faults due to occupant behavior •Energy savings from behaviors above and beyond “typical”• Role of automation • Occupant acceptance of approaches essential • Non-energy benefits and deficits •MELs a major need and challenge in low-energy homes

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