Publikationen von Dr. Konstantin Hopf

Hopf, K., Müller, O., Shollo, A. and Thiess, T. (2023), “Organizational Implementation of AI: Craft and Mechanical Work”, California Management Review, SAGE Publications, Thousand Oaks, Vol. 66 No. 1, pp. 23–47.

Hopf, K., Weigert, A. and Staake, T. (2022), “Value creation from analytics with limited data: a case study on the retailing of durable consumer goods”, Journal of Decision Systems, Taylor & Francis, Abingdon, No. Published online: 07 Apr 2022, pp. 1–37.

Shollo, A., Hopf, K., Thiess, T. and Müller, O. (2022), “Shifting ML value creation mechanisms: A process model of ML value creation”, The Journal of Strategic Information Systems, Elsevier, Amsterdam [u.a.], Vol. 31 No. 3.

Weigert, A., Hopf, K., Günther, S.A. and 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, Elsevier, Amsterdam [u.a.], Vol. 169 No. October, pp. 1–15.

Hopf, K. (2018), “Mining volunteered geographic information for predictive energy data analytics”, Energy Informatics, Springer, Berlin ; Heidelberg [u.a.], Vol. 1 No. 4.

Stingl, C., Hopf, K. and Staake, T. (2018), “Explaining and predicting annual electricity demand of enterprises – a case study from Switzerland”, Energy Informatics, Springer International Publishing, Cham, Vol. 1 No. Suppl. 1, p. 50.

Hopf, K., Sodenkamp, M. and Staake, T. (2017), “Enhancing energy efficiency in the residential sector with smart meter data analytics”, Electronic Markets, Springer, Berlin ; Heidelberg, No. (2018), First Online: 17 March 2018.

Hopf, K., Sodenkamp, M., Kozlovskiy, I. and Staake, T. (2016), “Feature extraction and filtering for household classification based on smart electricity meter data”, Computer Science - Research and Development, Springer, Berlin ; Heidelberg, Vol. 31, pp. 141–148.

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

Giacomazzi, E., Haag, F. and 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, ACM, New York, pp. 353–360.

Haag, F., Günther, S., Hopf, K., Handschuh, P., Klose, M. and 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, AISeL, AIS electronic library.

Haag, F., Stingl, C., Zerfass, K., Hopf, K. and 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, AISeL, New York, pp. 1–17.

Hopf, K., Hartstang, H. and 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), ACM digital library, pp. 32–39.

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

Fteimi, N. and Hopf, K. (2021), “Knowledge Management in the Era of Artificial Intelligence: Developing an Integrative Framework”, Association for Information Systems (AIS).

Wastensteiner, J., Weiss, T.M., Haag, F. and Hopf, K. (2021), “Explainable AI for Tailored Electricity Consumption Feedback: An Experimental Evaluation of Visualizations”, Association for Information Systems (AIS), pp. 1–19.

Weigert, A., Hopf, K., Weinig, N. and Staake, T. (2020), “Detection of heat pumps from smart meter and open data”, Energy Informatics, Vol. 3, Springer Science and Business Media {LLC}, p. 14.

Weigert, A., Hopf, K. and Staake, T. (2019), “A Cognitive Computing Solution to Foster Retailing of Renewable Energy Systems”.

Hopf, K., Kormann, M., Sodenkamp, M. and Staake, T. (2018a), “A Decision Support System for Photovoltaic Potential Estimation”, opus, Bamberg, doi: 10.20378/irbo-50523.

Hopf, K., Kormann, M., Sodenkamp, M. and Staake, T. (2018b), “A Decision Support System for Photovoltaic Potential Estimation”, Proceedings of the 1st International Conference on Internet of Things and Machine Learning, Liverpool, United Kingdom — October 17 - 18, 2017, ACM Digital Library, New York, pp. 1–10.

Hopf, K., Riechel, S.J., Sodenkamp, M. and Staake, T. (2018), “Predictive Customer Data Analytics: The Value of Public Statistical Data and the Geographic Model Transferability”, opus, Bamberg, doi: 10.20378/irbo-50522.

Sodenkamp, M., Kozlovskiy, I., Hopf, K. and Staake, T. (2017), “Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector”, Proceedings Der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), AIS Electronic Library (AISeL), pp. 1235–1249.

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

Hopf, K., Sodenkamp, M., Kozlovskiy, I. and Staake, T. (2016), “Household classification using annual electricity consumption data”, opus, Bamberg.

Kozlovskiy, I., Sodenkamp, M., Hopf, K. and 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), AIS Electronic Library (AISeL).

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

Hopf, K., Dageförde, F. and Wolter, D. (2015), “Identifying the Geographical Scope of Prohibition Signs”, in Fabrikant, S.I., Raubal, M., Bertolotto, M., Davis, C. and Scott, B. (Eds.), Spatial Information Theory: 12th International Conference, COSIT 2015 Santa Fe, NM, USA, October 12–16, 2015, Proceedings, Vol. Volume 9368; Lecture Notes in Computer Science, Springer International Publishing, Cham, pp. 247–267.

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.

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.

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.

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.