Resource-efficient buildings require commitment of electricity consumers. However, attempts to influence them in this direction are often based on simplistic analyses. It is often assumed, for example, that all electricity consumers know exactly what they should do to contribute to a more flexible and efficient use of electricity, that they have similar conditions in terms of drivers and barriers, and that they are equally affected by a certain price signal.
An alternative is to see the electricity market as consisting of distinct niches, where both the most effective action, how to respond to a price signal, and what drivers and barriers exist, can be very different.

In this project, we use statistical algorithms and machine learning to identify natural niches of electricity consumers based on data from their electricity consumption. For each niche, we intend to determine

1. what needs for control there exist in the local electricity grid and the most urgent measure for increased flexibility
2.  how the price signals affect these particular electricity consumers
3. the drivers and obstacles that exist for those electricity consumers in particular, and
4. the stage at which they are in terms of changing their behavior in the desired direction.

The aim is to identify policy measures that are more tailored to which behavioral changes or investments are most urgent, and that consider the factors that motivate and hinder these changes, in these niches.

The research project is being carried out within the framework of the interdisciplinary research group USER – Uppsala Smart Energy Research group at Uppsala University.