@article{barsanti2025103931,
Author = {Matteo Barsanti and Jan Sören Schwarz and Faten Ghali and Selin Yilmaz and Sebastian Lehnhoff and Claudia R. Binder},
Title = {Load-shifting for cost, carbon, and grid benefits: A model-driven adaptive survey with German and Swiss households},

Journal = {Energy Research \& Social Science},
Year = {2025},
Month = {01},
Series = {121},
Isbn = {2214-6296},
Doi = {https://doi.org/10.1016/j.erss.2025.103931},
Url = {https://www.sciencedirect.com/science/article/pii/S221462962500012X?via%3Dihub},
type = {article},
Abstract = {Survey data helps understand user energy behaviour and inform policies supporting the transition to a renewable, user-centric electricity grid. To explore user responses to dynamic, hypothetical energy scenarios – such as time-varying electricity tariffs or fluctuations in renewable energy availability – surveys often rely on standardised fixed-choice questions. However, these methods frequently oversimplify the complexity, diversity, and temporal dynamics of user behaviour, resulting in generalised and incomplete insights for interventions. To address these challenges, we introduce a model-driven adaptive survey. By integrating a conventional survey design with a feedback loop between participant responses and an energy demand model, this method allows end-users to iteratively evaluate and adjust their choices through a set of indicator scores. We implemented this approach in a survey conducted across Germany and German-speaking Switzerland (N=803), investigating user willingness to time-shift dishwashing usage under four scenarios: time-of-use tariffs, congestion risks, renewable energy availability, and their combinations. Our findings highlight the value of integrating energy demand models into survey designs to assist respondents in making complex energy-related decisions in a tailored manner. Respondents exhibited significant variability in their load-shifting practices, with over 56% reporting a likelihood of time-shifting energy use even without financial incentives. Participants using the feedback mechanism achieved notable improvements: 19% reduction in energy costs, 80% reduction in peak energy demand, and 9% increase in renewable energy usage on average for running the dishwasher. Beyond its utility for data collection, we discuss how this approach could extend to real-world applications, enabling users to navigate decision-making in increasingly dynamic energy systems.}
}
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