Research Front 3 tests different involvement strategies, focusing on experimenting different approaches to involve occupants to be more sober and more flexible in collective residential buildings. Different behavioral levers are going to be tested along with different experiments. One challenge of the PhD3 is about measuring the impact of each lever: while it is relatively easy to measure a lever impact on electricity consumption, it is difficult to assess an impact on energy used for heating (and possibly air conditioning). The assessments of the results follow 2 complementary approaches:
a) a verification methodology dedicated to the measurements of energy lever impacts by measuring over a few weeks period and extrapolating to a year period with a propagation of uncertainties 1, 2;
b) the analysis of household behavior regarding energy usage.
In an energy performance verification protocol (a), comparing predicted and measured data is a difficult task, especially due to uncertainties surrounding the building and its environment. To tackle this issue, we plan to extend a global approach aiming to qualify “real” performance versus expected one, by exploiting measured data (including weather and occupant’s behavior). This approach associates joint sensitivity and uncertainty analyses with a building dynamic thermal simulation model in order to gradually identify and then reduce the main sources of uncertainty on the energy consumption. A temporal analysis 2 will be used to identify most suitable measurement periods for the calibration of the main influential building model parameters {tooltip}3{end-texte}M. Robillart. Étude de stratégies de gestion en temps réel pour des bâtiments énergétiquement performants. Ph.D. thesis, École nationale supérieure des mines de Paris, 2015.{end-tooltip}
The approach (b) requires the use of fine observation techniques in an ecological environment. Often referred to as behavioral economics, the methodology envisaged in this research program requires special attention in order to minimize, or at least control, in situ observation biases. This methodological lock alone represents a real scientific challenge for an effective « demonstration of proof ». At the scientific level, the main obstacles are related to the development of incentive mechanisms (monetary and/or non-monetary) whose effectiveness is time-dependent and take into account the specificities of each household. Highly contextualized and inherently evolutionary energy behaviors must be studied in a real environment {tooltip}4{end-texte}D. Llerena, V. Lobasenko. Elicitation of Willingness to Pay for Upgradeable Products with Calibrated Auction-Conjoint Method, Journal of Environmental Planning and Management, 2017, 60 (11), pp.2036-2055.{end-tooltip}.
The project aims at evaluating engaging approaches impacts (e.g. on energy consumption or thermal comfort) and connecting them to changes in inhabitant’s practices. Methodological framework for comparing the engaging approaches will be developed as well. Some approaches will be based on energy performance verification protocol and calibration method .
A first set of experimentations is going to be done: the ones that do not require outputs from research fronts 2 and 3. They will be followed by a second set. The first set is the so called “large scale experiments” and the second set, so called “fine experimentation”.
We will start by testing a pure economical approach with the individualization of the heating bills using the technology developed by Kocliko to determine the heating consumption for each apartment of a collective using the measured ambient temperatures in all the dwellings of a building. This system enables a straightforward communication towards the inhabitants since the bill is directly linked to the ambient temperature which is more intuitive than kWh from individuals energy meters e.g. an inhabitant can see that it will pay 90€ a month to heat his dwelling 22°C, and 60€ per month to heat at 20°C. This allows setting up individualized objectives expressed in monetary saving or in CO2 emissions conservation. This information will be completed by a detailed report analyzing the occupants’ behavior in homes.
While the first set of experiment on the individualization of heating bills is essentially based on individual economic determinants of behavior, the involvement of households in the management of their energy consumption can also be achieved through collective dynamics. These dynamics explicitly introduce social interactions between the inhabitants of the same building or of a group of buildings. Beyond purely individual logic, and according to recent behavioral approaches {tooltip}5{end-texte}D. Llerena,, Roussillon, B. and Teyssier, S., Delinchant, B., Ferrari, J., Laranjeira, T., and Wurtz, F. Buckley, P. Demand response in the workplace : A Field experiment, WP GAEL 2021-01.{end-tooltip}, {tooltip}6{end-texte}Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.{end-tooltip}, {tooltip}7{end-texte}Ayres, I., Raseman, S., & Shih, A. (2013). « Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage ». The Journal of Law, Economics, and Organization, 29(5), 992–1022.{end-tooltip}, these interactions can be the support of motivational mechanisms (often non-monetary, i.e. nudges), learning processes (learning from others) and stimulation of changes (fun, challenge, …). Thus, as a complement to the first experiment, we propose to carry out experimental studies based on a gaming approach such as tournaments versus contests.
The advanced IHEMAS developed by RF1 in cooperation with RF2 will be tested thanks to the methods to measure changes in inhabitant’s practices.
1.. G. A. Faggianelli, L. Mora, R. Merheb. Uncertainty quantification for Energy Savings Performance Contracting: Application to an office building. Energy and Buildings, 152, 61-72, 2017 ↩
2.. T. Recht, J. Goffart, L. Mora, C. Buhé, M. Wolozyn. Predicted and measured performances of near zero-energy houses: a com- parison methodology. Proceedings of 16th IBPSA conference, Rome, Italy. International Building Performance Simulation Association (IBPSA), 2019.↩↩
3.. M. Robillart. Étude de stratégies de gestion en temps réel pour des bâtiments énergétiquement performants. Ph.D. thesis, École nationale supérieure des mines de Paris, 2015. ↩
4.. D. Llerena, V. Lobasenko. Elicitation of Willingness to Pay for Upgradeable Products with Calibrated Auction-Conjoint Method, Journal of Environmental Planning and Management, 2017, 60 (11), pp.2036-2055. ↩
5.. D. Llerena, Roussillon, B. and Teyssier, S., Delinchant, B., Ferrari, J., Laranjeira, T., and Wurtz, F. Buckley, P. Demand response in the workplace : A Field experiment, WP GAEL 2021-01. ↩
6.. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. ↩
7.. Ayres, I., Raseman, S., & Shih, A. (2013). « Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage ». The Journal of Law, Economics, and Organization, 29(5), 992–1022. ↩