AsimutE: Intelligent self-consumption and storage for better use of energy
- Contact:
Dr. Thomas Dengiz
Dr. Daniel Sloot
M.Sc. Stephanie Stumpf
M.Sc. Max Kleinebrahm
Dr. Manuel Ruppert - Funding:
- Partner:
Université de Haute-Alsace, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Hochschule Offenburg, Hochschule Furtwangen, Centre National de la Recherche Scientifique, Hochschule Kehl, Albert-Ludwigs-Universität Freiburg, Fachhochschule Nordwestschweiz FHNW
- Startdate:
10/2023
- Enddate:
01/2027
Project Details
The ASIMUTE project investigates solutions for optimized and safe energy use and storage by involving end users throughout the project. The aim is to achieve a balance between energy demand and the production capacity of renewable energies, taking into account the available storage options. The project partners will use artificial intelligence methods and conduct surveys among consumers, energy suppliers and stakeholders in the Upper Rhine region. The project is multidisciplinary as it will cover aspects from both a techno-economic and a social science perspective. The legal feasibility in the trinational context as well as the acceptance by end consumers in the different cultural contexts of the three countries will be investigated. This will be based on findings from sociological, legal and technical studies that have emerged from the Interreg projects “Vehicle” and “Advanced Control Algorithms for the Management of Decentralised Energy Systems”.
The DFIU is involved in several parts of the project. Together with the Université de Haute Alsace, the effectiveness of calls to save energy in private households is being investigated, taking into account psychological compensation mechanisms. In addition, the expectations of private households with regard to technologies for self-consumption of energy are being investigated with the help of qualitative and quantitative empirical studies.
The DFIU is also involved in the development of methods for the multi-objective optimization of heating systems in representative residential areas of the respective countries. In addition to energy costs, greenhouse gas emissions, thermal comfort and electrical load peaks are optimized in simulations. As the objectives in a residential area are often contradictory, multi-objective optimization methods in combination with machine learning methods are particularly suitable.