The purchase of sustainable energy products such as locally produced renewable electricity, photovoltaic systems or heat pumps are measures that allow consumers to make a significant contribution to transforming our energy systems. Yet the willingness of consumers to pay more for electricity or make energy-related investments is lagging behind what energy policy makers have expected. To counteract this problem, the SmartLoad project has developed data analysis methods that help energy retailers to promote such sustainable consumption behavior among their customers.
The project group of the University of Bamberg (Chair of Information Systems and Energy Efficient Systems), together with the two Swiss companies BEN Energy AG and Centralschweizerische Kraftwerke AG, has analyzed customer data, electricity consumption data and free geographic information. They developed a software that enables energy retailers to offer tailored tariffs and services. At the core of the solution are methods of machine learning, which automatically recognize patterns in data and enable targeted forecasts for individual customers. The solution was tested together with the energy utility in three use cases.
In the first application case, "energy efficiency", the research group showed that it is possible, for example, to identify households with high base load consumption and thus make targeted savings recommendations for consumers. For the two application cases "sales of local green electricity" and "sales of photovoltaic, battery storage systems, and heat pumps", sales activities were supported in such a way that particularly interested customers were identified.
The project was financially supported for 34 months as part of the ERA-Net SmartGrid Plus Initiative by the Federal Ministry of Economics and Energy (Germany) and the Federal Office of Energy (Switzerland).