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 40th Annual IAEE International Conference Singapore, 21 June 2017

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

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

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

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)

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

Results: average marginal effects

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

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

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 > 12

Thank you! Grenoble Ecole de Management 12 Pierre Sémard France joachim.schleich@grenoble-em.com Fraunhofer Institute Systems- & Innovation Research Breslauer Straße 48 76139 Karlsruhe Germany joachim.schleich@isi.fraunhofer.de