1. Persistence of the effects of providing feedback alongside smart metering devices on household electricity demand Joachim Schleich Grenoble Ecole de Management, Fraunhofer ISI Corinne Faure Grenoble Ecole de Management Marian Klobasa Fraunhofer ISI th Annual IAEE International Conference Singapore, 21 June 2017

2. Introduction EU Target:
EU ESD: roll out of smart meters to 80 % by 2020
Most countries are on track (EC 2020)
Providing feedback via in-house-displays (IHD) has been found to be effective in several pilot studies:
Gans et al. (2013; Northern Ireland: 11-17%); Gleerup et al. (2010; Dk: 3%); Schleich et al. (2013; A: 5.5%); Houde et al. (2013; US: 5.7%)
But: providing feedback on electricity use alone may lead to small reductions only
Tedenvall and Mundaca (2016), Delmas (2016): 2%
IHD should be implemented with other measures
Abrahamse et al.(2005): feedback is particularly effective when it is provided together with information on energy-efficiency measures
Germany: 80 per cent by 2050

3. Objective What are effects of providing feedback together with IHD?
Do the effects persist or disappear over time?
Does feedback lead to changes in usage profile (for instance, reduction of the base load)?
Provide insights whether feedback leads to (permanent/transitory) changes in habitual behaviors and investment in energy-efficient technologies
Previous literature:
Houde et al. (2013) (non-random sample: google employees from California): effects mainly during peak hours, but disappear after 4 weeks
Feedback leads to transitory changes in habitual behavior

4. Data Field trial in city of Linz, Austria
December 2009 to November 2010
1525 observations , randomly selected into
775 in pilot group, smart meter plus feedback
750 in control group, smart meter no feedback
Pilot group
Web-portal (electricity consumption patterns and electricity costs)
Written information by post once a month (daily, weekly, and monthly household electricity consumption)
Both groups received information on how to lower electricity use
Hourly data for individual household electricity consumption
Cross section data available only
Survey on socio-economic and technology characteristics

5. Econometric models
1) Average effects of providing feedback on electricity use
electricity is the (log of) electricity use by household i at hour t of a day (t=1-24) and c is a constant
electricity is calculated as the average electricity consumption at hour t per month
feedback is a dummy variable indicating that household i received feedback on electricity consumption
 captures average effect, i.e. difference between those receiving feedback and those who did not (in %)
Zi is a vector of household socio-economic and appliance stock characteristics (which do not vary over time).
distinguish between weekdays (Monday to Friday) and weekend days (Saturday and Sunday)
do not distinguish between web-based and postal feedback (based on tests)
identification: assume conditional independence
GLS RE model, cluster robust SE (by households)

6. Econometric models
2) Persistence: does effectiveness of feedback decrease over time?
3) Does effectiveness of feedback vary over course of the day ?
measures the percentage difference in hourly electricity consumption
between households that received feedback and those that did not in month m
measures the percentage difference in hourly electricity consumption
between households that received feedback and those that did not in hour h

7. Results: average marginal effects

8. Results (weekdays): persistence
Feedback effect appears to be persistent over the course of the field trial

9. Results (weekdays): variation over course of the day
(weak) evidence that feedback not only lowers peak load but also base load

10. Conclusion Providing feedback is effective
Estimate of average feedback effect of around 5% is at lower end of spectrum
No difference in percentage terms across weekdays and weekend days
Feedback effect appears to be persistent over the course of the field trial
suggests permanent change in habitual behavior or investment
(Weak) evidence that feedback not only lowers peak load but also base load
suggests investment
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11. Thank you! Grenoble Ecole de Management 12 Pierre Sémard
France
Fraunhofer Institute Systems-
& Innovation Research Breslauer Straße Karlsruhe Germany