Konstantin Hopf still maintains close ties to his former working group, where he worked as a doctoral student, established an independent research group as a postdoctoral researcher, and where he played a key role in shaping research and teaching. After graduating in Information Systems at the University of Bamberg, he focused on the use of machine learning methods in energy retail and energy efficiency.

After completing his doctorate at our chair in 2019, he continued his work as a senior researcher and lecturer. In this role, he acquired and led industry-related research projects, was responsible for highly rated courses in the Master's programs in Information Systems, and completed his habilitation in October 2025.

At Chemnitz University of Technology, Prof. Hopf will deepen and further develop these topics in research and teaching. These include embedding business analytics methods in sociotechnical systems and applying explainable AI methods for decision support in organizations.

We congratulate Prof. Dr. Konstantin Hopf on his appointment, wish him continued success in his future academic and scientific work, and look forward to future collaborations within the Bits-to-Energy Lab!

In addition to Dr. Konstantin Hopf and Prof. Thorsten Staake, the colleagues Prof. Nora Nahr and Prof. Franz Lehner from the University of Passau collaborated in the study.

The article is available as open access in the Journal of Information Technology.

DOAG.tv

• Interview by Dr. Konstantin Hopf together with Dr. Benjamin Linnik

Telekom LEX Learning from Experts

• Lecture series"KI evolution instead of revolution - A process model for operational value creation through machine learning applications"
• May 8 and 15, 2024, with approx. 90 participants each time, online

QualityMinds Lunch & Learn

• Presentation "Value Creation with Machine Learning"
• April 10, 2014, with approx. 25 participants, online

DOAG AI Navigator Conference

• Program contribution "From theory to practice: Case studies on AI value creation in companies" together with Dr. Benjamin Linnik
• November 22, 2023, with approx. 80 trade visitors, Messe Nürnberg

SAP Inspiration Sessions

• Keynote "Process Model for Machine Learning Value Creation in Organizations"
• November 15, 2023, with approx. 250 international participants, online

In the practical presentations, the research findings will be explained using practical examples. If you are interested in further presentations or workshops in the field of AI value creation, please contact Dr. Konstantin Hopf directly.

Abstract: Due to the advent of digital learning environments and the freedom they offer for learners, new challenges arise for students' self-regulated learning. To overcome these challenges, the provision of feedback has led to excellent results, such as less procrastination and improved academic performance. Yet, current feedback artifacts neglect learners’ heterogeneity when it comes to prescriptive feedback that should meet personal characteristics and self-regulated learning skills. In this paper, we derive requirements from self-regulated learning theory for a feedback artifact that takes learners’ heterogeneity into account. Based on these requirements, we design, instantiate, and evaluate an Explainable AI-based approach. The results demonstrate that our artifact is able to detect promising patterns in data on learners' behaviors and characteristics. Moreover, our evaluation suggests that learners perceive our feedback as valuable. Ultimately, our study informs Information Systems research in the design of future Explainable AI-based feedback artifacts that seek to address learners' heterogeneity.

[Link]

The study investigates how much saving can be achieved in a typical energy-saving campaign for heat pumps and how data from smart electricity meters can help to pre-select heat pumps with a particularly high energy-saving potential.

Heat pumps have become the heating system of the future and are being installed in an increasing number of buildings: At the same time, however, this heating technology is prone to planning, installation and setting errors, which in many cases leads to a reduction in efficiency and thus to an increase in electricity consumption and corresponding operating costs. In many cases, energy-saving consultants can also optimise the settings of the heat pump afterwards and raise efficiency potentials, but they need information in advance about which installations can particularly gain from consulting.

Our study indicates that it is already possible today to identify inefficiently operating heat pumps with the widely available smart metering infrastructure and exemplifies the potential of digitising energy consulting for heat pumps.

Link to the publication

This work is now being continued in two newly acquired research projects:

The BENEFIZZO project will develop ML methods for targeted energy efficiency feedback and the identification of suitable energy products for existing customers of energy providers. The project is funded between December 2020 and May 2022 by the EU's Eurostars program and industry partner Advizo Ltd. (London) and BEN Energy GmbH (Munich, Zurich).

The DigiSWM project investigates how ML and advanced data analytics can be used to develop and field test new energy services in the coupling of the consumption sectors electricity, heat and mobility. Supported by the IUK Bayern program, the research group cooperates in this project with the Junior professorship for Energy Informatics at the Friedrich-Alexander University Erlangen-Nuremberg, the regional utility Stadtwerk Haßfurt GmbH, the technology companies Consolinno Energy GmbH and BEN Energy GmbH as well as the heat pump manufacturer Hoval GmbH. The project will take place between July 2021 and September 2024.

Further information on both projects can be found on the Current Projects page and in the research information system FIS.

Please contact the project manager of both projects Dr. Konstantin Hopf for any inquiries about the research.

Our results indicate that such corporate carbon offset programs may actually induce adverse behavior by dampening individuals’ curtailment efforts. Besides that, our results suggest that feedback may serve as a remedy to counteract this adverse behavior.

Link to the publication