Project

The transition towards sustainability requires effective policies that trigger changes in economic decisions by households and companies. This project addresses several key questions concerning policies targeting household energy use, energy use in firms, demand flexibility, and mobility behavior. It focuses on experimental methods and deep learning algorithms to demonstrate how machine learning (ML) on spatio-temporal data can be utilized to increase the effectiveness of policies through targeting. Targeting policies is typically understood as targeting those individuals who are expected to have the greatest behavioral response to the policy. We complement this view by targeting policies at the right time, such as peak hours on the power grid or rush hours on the road, and at the right place, such as in certain congested power distribution networks or on certain highways or city districts.

In particular, we conduct large-scale surveys and field experiments to investigate the effectiveness of promising innovative interventions aimed at changing individual behavior. We leverage ML models such as deep neural networks to uncover the sensitivity of intervention effects with respect to personal characteristics as well as spatial and temporal circumstances.

As a further step, we investigate how these findings can help to identify target groups in relevant populations outside the experimental setting, which leads to improved cost effectiveness of targeted interventions. ML methods trained on the basis of the field experiment are used to make predictions about the impact of such targeted interventions at the individual level. However, such predictions require generalizations of the patterns found in the training data. From an ML perspective, this poses a major challenge, as the populations to be generalized over may differ greatly from the training data. A particular focus of the project therefore lies on the development and the application of deep learning methods that allow for such generalizations to succeed with as few additional data requirements as possible.  

TRR 391 webpage

Project start:
01. October 2024

Project end:
30. June 2028

Project management:
Prof. Dr. Mark Andor, Prof. Dr. Andreas Löschel (Ruhr-Universität Bochum), Prof. Dr. Asja Fischer (Ruhr-Universität Bochum)

Project staff:
Eva Hümmecke

Project partners:
Ruhr-Universität Bochum, Technische Universität Dortmund, Universität Hamburg, Universität Münster, Karlsruher Institut für Technologie, Technische Universität Hamburg, FH Dortmund

Funding:
Deutsche Forschungsgemeinschaft