Duration: 01.06.2017 — 31.03.2020
Total Budget: 802'378 € (industry and public funding)
Project Partners:
Funding:
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.
Funded by the Bayerische Verbundforschungsprogramm, F?rderlinie Digitalisierung – Informations- und Kommunikationstechnologie
Project duration: 01.07.2021 – 31.06.2024
Research staff at the University of Bamberg: Konstantin Hopf (Principal Investigator), Felix Haag (Project Associate)
Project Partners:
Total project budget: € 1'486'731 (funding and industry contribution), € 218'500 of the total budget for the University of Bamberg
Utilities have a large customer base, yet their knowledge about individual households is small. This adversely affects both the development of innovative, household specific services and the utilities’ monetary KPIs. Our software solutions help utility companies to engage their customers in energy saving campaigns and support them in selling respective services.
In this project, we develop further and test in field experiments machine learning algorithms that infer household characteristics (apartment size, number of inhabitants and appliances, etc.) and predict the willingness to participate in efficiency or load shifting campaigns from load profiles, location information, and other existing customer data. Our tools provide customer insights at low cost and at scale, thereby solving an eminent business problem, improving the effectiveness of energy conservation campaigns, and ultimately increasing the customer value and adoption of related services.
Funded by the EU Eurostars Programme
Research staff at the University of Bamberg: Konstantin Hopf
Project duration: 01.11.2015 - 30.10.2018
Project Partners:
Total Budget: 818'840 € (industry and public funding)
Smart electricity meters allow capturing consumption load profiles of residential buildings. The goal of the project is to develop methods of machine learning that will automatically infer energy efficiency relevant household characteristics (e.g., type of heating, living area, number of inhabitants per dwelling, etc.) from the power load traces. The gained information can be used by utilities as a starting point for developing energy consulting services, and for optimisation of sales channels.
The project will result to a new methodology and a software that will be employed by the customers of our partner BEN Energy already in 2016.
Funded by Commission for Technology and Innovation, Switzerland
Research staff at the University of Bamberg: Konstantin Hopf, Ilya Kozlovskiy, Dr. Mariya Sodenkamp
Project duration: 01.05.2014 - 30.04.2016
Project Partners:
Budget: CHF 442'560
Smart electricity meters allow for capturing consumption data of individual households at a high resolution in time (typically with 15-minute intervals). The key objective of this project is to develop further and evaluate feature extraction and machine learning techniques for automatic identification of household properties based on electricity load profiles. The gained information shall render highly targeted and scalable energy efficiency services possible.
Funded by Swiss Federal Office of Energy and Arbon Energie AG, Switzerland
Research staff at the University of Bamberg: Konstantin Hopf, Ilya Kozlovskiy, Dr. Mariya Sodenkamp
Project duration: 01.04.2014 - 31.01.2016
Project Partners:
Budget: CHF 353'450
The EU Project DAIAD deals with the structure and the testing of an IT infrastructure in order to capture and analyze the consumption of (warm) water in private households. In association with Amphiro AG (CH), WaterWise (UK), the Athena Research (GR), the Frauenhofer Institute for “Systems and Innovation Research” and the City of Athens, the research group from Bamberg tests concepts for motivating sustainable consumer behavior.
Funded by EUs Seventh Framework Programme for Research
Project coordinator: Prof. Dr. Thorsten Staake ; Dipl.-Kffr. Anna Kupfer
Project duration: 01.03.2014 - 31.08.2017
Project Partner:
Budget: EUR 3'202'288
Customer satisfaction and confidence are the corner stones of long-term, successful and sustainable relationships between utility companies and energy consumers. The goal of the project is to develop methods for analysis of consumption and behavioral data that enable segmentation of end energy consumers in the residential sector. The derived information serves utility companies as a basis for targeted efficiency interventions and confidence building dialog with customers by intelligent linking of customer data.
Funded by RWE AG
Research staff at the University of Bamberg: Ilya Kozlovskiy, Dr. Mariya Sodenkamp
Project duration: 01.10.2013 - 31.03.2015
Projektpartner: BEN Energy AG
The project Sense4EN is funded by the European Union and part of the European ClimateKIC initiative. The Energy Efficient Systems Group elaborates concepts for reducing heat consumption (especially warm water) by means of Smart Metering systems in association with ETH Zurich, TU Berlin and Vattenfall.