DigiSWM – AI and advanced data analytics for an interplay between power, heat and mobility
The integration of renewable energy sources in the private sector is made possible in particular by coupling the consumption sectors of electricity, heat and mobility. In order to leverage the enormous potential, energy solutions for the private sector must be consistently thought through. However, sector coupling and the digitization required for it also increase the demands on the actors in the development, parameterization, optimization and marketing of the technologies. Comprehensive energy data (from systems, consumption, and behavior) and AI processes can help to enable new energy services, optimize grid operations, and promote greater dissemination of sustainable energy technologies. The project aims to leverage the potential from existing energy data for such applications. The project's field-tested Big Data Analytics (BDA) toolbox will support households and utilities with machine learning technology to drive sector coupling.
Project duration: 01.07.2021 – 31.06.2024
- Friedrich-Alexander-Universität Erlangen-Nürnberg
- BEN Energy GmbH
- Consolinno Energy GmbH
- Stadtwerk Haßfurt
- Hoval GmbH
Total project budget: € 1'486'731 (funding and industry contribution), € 218'500 of the total budget for the University of Bamberg
BENEFIZZO – Combining behavioural and analytical innovation to enhance smart meter residential energy savings (machine learning for energy efficiency feedback)
By applying machine learning techniques to large amounts of data on private energy consumption and using advanced feedback technologies, this collaborative project builds a customer engagement platform as a mature prototype. This digital platform enables action-oriented communication tailored to the individual household, which guides private households to greater energy efficiency. Building on the energy efficiency platform to be developed, we will work with energy providers in Europe to create novel services that increase energy efficiency and sustainable energy use in the private sector. In doing so, we address the limits of smart meter technology recognized by companies, research and politics, as well as those of private energy consumption behavior.
The subproject of the University of Bamberg comprises the (further) development and evaluation of machine learning methods that form the basis for the smart energy efficiency platform to be developed. We are advancing machine learning models that can predict household characteristics (apartment size, number of occupants or appliances, etc.) or customers' willingness to participate in energy efficiency or load shifting campaigns using load curves, location information, and other data sources. In addition, we evaluate Explainable AI techniques for their use within the scope of energy feedback.
Funded by the Eurostars Program of the European Union
Project duration: 01.12.2020 – 31.05.2022
Total project budget: € 899'680 (funding and industry contribution), € 175'521 of the whole budget for the University of Bamberg