Peer-reviewed paper –Beyond clustering: rethinking the segmentation of energy consumers when nudging them towards energy-saving behavior (2022)

Besides technological innovations in energy production and management technologies, the fight against climate change requires fundamental changes in our energy consumption behavior. Behavioral interventions are key to this process, especially when tailored to different energy consumer segments accounting for their socio-demographic profiles, socio- psychological characteristics and energy consumption practices. In this work, we propose a novel approach to energy consumer segmentation that facilitates the choice of (nudging) interventions for each segment.

We call it intervention-driven energy consumer profiling since it explicitly considers upfront the set of interventions that can be delivered to energy consumers and defines profiles that can be readily matched with them. The profiles are specified as combinations of socio-psychological factors with implications for energy-saving behavior and are parameterized by thresholds that measure how strongly these factors are represented in each profile.

One profile represents ideal energy-savers, whereas each of the remaining five profiles shares one or two distinct features that serve as barriers towards energy-saving behavior and/or prescribe specific type of nudging interventions for strengthening such behavior. We use the responses of users to a European-wide online survey to formulate and solve an optimization problem for these thresholds and then assign the survey respondents to the profiles.

Finally, we analyze them also in terms of socio-demographic variables and recommend appropriate nudging interventions for them.


Merkourios Karaliopoulos (Athens University of Economics and Business); Leonidas Tsolas (Athens University of Economics and Business); Maria Halkidi (University of Piraeus); Iordanis Koutsopoulos (Athens University of Economics and Business); Stephanie Van Hove (Ghent University); and Peter Conradie (Ghent University)

Published in the ACM SIGENERGY Energy Informatics Review