Publikationen von Dr. Konstantin Hopf

Hopf, K., Nahr, N., Staake, T., & Lehner, F. (2024). The group mind of hybrid teams with humans and intelligent agents in knowledge-intense work. Journal of Information Technology, Online first: October 22, 2024. https://doi.org/10.1177/02683962241296883

Hopf, K., Müller, O., Shollo, A., & Thiess, T. (2023). Organizational Implementation of AI: Craft and Mechanical Work. California Management Review, 66(1), 23–47. https://doi.org/10.1177/00081256231197445

Hopf, K., Weigert, A., & Staake, T. (2022). Value creation from analytics with limited data: a case study on the retailing of durable consumer goods. Journal of Decision Systems, Published online: 07 Apr 2022, 1–37. https://doi.org/10.1080/12460125.2022.2059172

Shollo, A., Hopf, K., Thiess, T., & Müller, O. (2022). Shifting ML value creation mechanisms: A process model of ML value creation. The Journal of Strategic Information Systems, 31(3). https://doi.org/10.1016/j.jsis.2022.101734

Weigert, A., Hopf, K., Günther, S. A., & Staake, T. (2022). Heat pump inspections result in large energy savings when a pre-selection of households is performed: A promising use case of smart meter data. Energy Policy, 169(October), 1–15. https://doi.org/10.1016/j.enpol.2022.113156

Hopf, K. (2018). Mining volunteered geographic information for predictive energy data analytics. Energy Informatics, 1(4). https://doi.org/10.1186/s42162-018-0009-3

Stingl, C., Hopf, K., & Staake, T. (2018). Explaining and predicting annual electricity demand of enterprises – a case study from Switzerland. Energy Informatics, 1(Suppl. 1), 50. https://doi.org/10.1186/s42162-018-0028-0

Hopf, K., Sodenkamp, M., & Staake, T. (2017). Enhancing energy efficiency in the residential sector with smart meter data analytics. Electronic Markets, (2018), First Online: 17 March 2018. https://doi.org/10.1007/s12525-018-0290-9

Hopf, K., Sodenkamp, M., Kozlovskiy, I., & Staake, T. (2016). Feature extraction and filtering for household classification based on smart electricity meter data. Computer Science - Research and Development, 31, 141–148. https://doi.org/10.1007/s00450-014-0294-4

Hopf, K. (2024). KI-Wertschöpfung in Unternehmen: Evolution statt Revolution. Red Stack Magazin, 4, 18–21.

Hopf, K., & Linnik, B. (2024). Phönix aus der Asche: KI braucht langfristige Perspektiven. Heise online, KI Navigator #4.

Bayer, D. R., Haag, F., Pruckner, M., & Hopf, K. (2024). Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study. Proceedings of the 9th International Conference on Smart and Sustainable Technologies 2024 (SpliTech), 10, 1–6. https://doi.org/10.23919/splitech61897.2024.10612563

Hopf, K., Joshi, M., Stelmaszak, M., & Shollo, A. (2024). Crafting Ever-Changing Data Products: Towards a Human-Centered Process Model of Data Work. ECIS 2024 Proceedings, 1–17.

Giacomazzi, E., Haag, F., & Hopf, K. (2023). Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources. Proceedings of the 14th ACM International Conference on Future Energy Systems, 353–360. https://doi.org/10.1145/3575813.3597345

Haag, F., Günther, S., Hopf, K., Handschuh, P., Klose, M., & Staake, T. R. (2023). Addressing Learners’ Heterogeneity in Higher Education: an Explainable AI-based Feedback Artifact for Digital Learning Environments. Proceedings of the 18th Internationale Tagung Wirtschaftsinformatik.

Haag, F., Stingl, C., Zerfass, K., Hopf, K., & Staake, T. (2023). Overcoming Anchoring Bias: the Potential of AI and XAI-based Decision Support. Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies: ICIS 2023, Proceedings, 1–17.

Hopf, K., Hartstang, H., & Staake, T. R. (2023). Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting. Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy), 32–39. https://doi.org/10.1145/3599733.3600248

Haag, F., Hopf, K., Menelau Vasconcelos, P., & Staake, T. (2022). Augmented Cross-Selling Through Explainable AI: a Case From Energy Retailing. ECIS 2022 Proceedings, 1–19.

Fteimi, N., & Hopf, K. (2021). Knowledge Management in the Era of Artificial Intelligence: Developing an Integrative Framework. ECIS 2021 Proceedings.

Wastensteiner, J., Weiss, T. M., Haag, F., & Hopf, K. (2021). Explainable AI for Tailored Electricity Consumption Feedback: An Experimental Evaluation of Visualizations. ECIS 2021 Proceedings, 1–19.

Weigert, A., Hopf, K., Weinig, N., & Staake, T. (2020). Detection of heat pumps from smart meter and open data. Energy Informatics, 3(1), 14. https://doi.org/10.1186/s42162-020-00124-6

Weigert, A., Hopf, K., & Staake, T. (2019). A Cognitive Computing Solution to Foster Retailing of Renewable Energy Systems. https://doi.org/10.20378/IRB-47040

Hopf, K., Kormann, M., Sodenkamp, M., & Staake, T. (2017). A Decision Support System for Photovoltaic Potential Estimation. IML ’17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning, Article No.: 3, 1–10. https://doi.org/10.1145/3109761.3109764

Hopf, K., Riechel, S. J., Sodenkamp, M., & Staake, T. (2017). Predictive Customer Data Analytics: The Value of Public Statistical Data and the Geographic Model Transferability. ICIS 2017 Proceedings, 1–20.

Sodenkamp, M., Kozlovskiy, I., Hopf, K., & Staake, T. (2017). Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector. Proceedings Der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), 1235–1249.

Hopf, K., Sodenkamp, M., & Kozlovskiy, I. (2016). Energy Data Analytics for Improved Residential Service Quality and Energy Efficiency. Proceedings of the 24. European Conference on Information Systems (ECIS).

Hopf, K., Sodenkamp, M., Kozlovskiy, I., & Staake, T. (2016). Household classification using annual electricity consumption data.

Kozlovskiy, I., Sodenkamp, M., Hopf, K., & Staake, T. (2016). Energy Informatics for Environmental, Economic and Societal Sustainability: A Case of the Large-Scale Detection of Households with Old Heating Systems. Proceedings of the 24. European Conference on Information Systems (ECIS).

Hopf, K., Dageförde, F., & Wolter, D. (2015). Identifying the Geographical Scope of Prohibition Signs. Spatial Information Theory: 12th International Conference, COSIT 2015 Santa Fe, NM, USA, October 12–16, 2015, Proceedings, 247–267. https://doi.org/10.1007/978-3-319-23374-1_12

Hopf, K. (2019), Predictive Analytics for Energy Efficiency and Energy Retailing, University of Bamberg Press, Bamberg, doi: 10.20378/irbo-54833.

Sodenkamp, M., Hopf, K. and Staake, T. (2015), “Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing”, in Tavana, M. and Puranam, K. (Eds.), Handbook of Research on Organizational Transformations through Big Data Analytics, IGI Global, Hershey, Pennsylvania (USA), pp. 320–333, doi: 10.4018/978-1-4666-7272-7.

Weigert, A., Hopf, K. and Staake, T. (2020), Analyseverfahren zur Erkennung von Effizienz- und Selbstversorgungspotenzialen individueller Haushalte: Schlussbericht zum Teilprojekt 0350010 im Verbundvorhaben “SmartLoad”; Projektlaufzeit: 01.06.2017-31.03.2020, Technische Informationbibliothek (TIB), Hannover, doi: 10.2314/KXP:1754715761.

Weigert, A., Hopf, K., Staake, T., Rast, A. and Marckhoff, J. (2020), SmartLoad: Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector, Bundesamt für Energie, Sektion Energieforschung und Cleantech, Bern.

Hopf, K. and Staake, T. (2019), Methoden der Energiedatenanalyse: Schlussbericht; Vorhaben im Rahmen des Eurostars Projekts E! 9859 BENginell “Energy Data Analytics: Increasing Service Quality and Energy Efficiency in the Residential Sector”, TIB, Hannover, doi: 10.2314/KXP:1687331642.

Sodenkamp, M., Hopf, K., Kozlovskiy, I. and Staake, T. (2016), Smart-Meter-Datenanalyse für automatisierte Energieberatungen (“Smart Grid Data Analytics”) - Schlussbericht, opus, Bamberg.

Hopf, K. and Reifenrath, S. (2021), “Filter Methods for Feature Selection in Supervised Machine Learning Applications: Review and Benchmark”, arXiv, doi: 10.48550/arxiv.2111.12140.