Weitere Publikationen finden Sie auf der vollst?ndigen Publikationsliste von Konstantin Hopf.
Zeitschriftenbeitr?ge (peer-reviewed)
Hopf, K., Müller, O., Thiess, T., Shollo, A. (2023). Organizational implementation of AI: Craft and mechanical work. California Management Review 66(1).
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), 101734. https://doi.org/10.1016/j.jsis.2022.101734; ausgezeichnet mit dem JSIS 2022 Best paper award im M?rz 2023
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, 113156. https://doi.org/10.1016/j.enpol.2022.113156
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, Online ver?ffentlicht am 07. April 2022, DOI: 10.1080/12460125.2022.2059172
Hopf, K., Sodenkamp, M., Staake, T. (2018). Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector. Electronic Markets, 28(4) DOI: 10.1007/s12525-018-0290-9; ausgezeichnet mit dem AIS SIGGREEN 2018 Best Journal Paper on Green ISaward im Dezember 2018
Hopf, K. (2018). Mining Volunteered Geographic Information for Predictive Energy Data Analytics. Energy Informatics, 1:4, DOI: 10.1186/s42162-018-0009-3
Beitr?ge in Konferenzb?nden (peer-reviewed)
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. 32. European Conference on Information Systems, Paphos: Zypern.
Haag, F., Stingl, C., Zerfass, K., Hopf, K., Staake, T. (2023). Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support, 44. International Conference on Information Systems 10. - 13. Dezember, Hyderabad: Indien
Haag, F., Günther, S. A., Hopf, K., Handschuh, P. Klose, M., Staake, T. (2023). Addressing Learners' Heterogeneity in Higher Education: An Explainable AI-based Feedback Artifact for Digital Learning Environments. 18. Internationale Tagung Wirtschaftsinformatik 18. - 21. September, Paderborn; WI'24 Best Paper Award.
Hopf, K., Hartstang, H., Staake, T. (2023). Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting. Vorgestellt auf dem 4. International Workshop on Energy Data and Analytics im Rahmen der 14. ACM e-Energy Konferenz, 20. Juni, Orlando:Florida (USA). DOI: 10.1145/3575813.3597345 [Preprint]
Giacomazzi, E., Haag, F., Hopf, K. (2023). Short-term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources. Vorgestellt auf der 14. ACM e-Energy Konferenz, 20. Juni, Orlando:Florida (USA). DOI: 10.1145/3599733.3600248 [Preprint]
Günther, S. A., Haag, F., Hopf, K., Klose, M., Handschuh, P., Staake, T. (2022). A feedback component that leverages counterfactual explanations for smart learning support: First insights into its empirical evaluation. Tagungsband DiKuLe Symposium 2022 (in Erscheinung)
Haag, F., Hopf, K., Menelau Vasconcelos, P., Staake, T. (2022). Augmented Cross-Selling Through Explainable AI – A Case From Energy Retailing. 30. European Conference on Information Systems (ECIS'22), Timi?oara: Romania [Full-text] [Preprint]
Wastensteiner, J., Weiss, T. M., Haag, F., Hopf, K. (2021). Explainable AI for Tailored Electricity Consumption Feedback – An Experimental Evaluation of Visualizations, 29. European Conference on Information Systems (ECIS'21), Marrakesh: Morocco / Virtual, 14. – 12. Juni, [Full-text] [Preprint]
Weigert, A., Hopf, K., Weinig, N., Staake, T. (2020) Detection of heat pumps from smart meter and open data, 9. DACH+ Conference on Energy Informatics, Sierre, Schweiz, 29. – 30. Oktober, In: Energy Informatics, 3(Suppl 1):21, DOI: 10.1186/s42162-020-00124-6
Hopf, K., Riechel, S., Sodenkamp, M., Staake, T. (2017). Predictive Customer Data Analytics – The Value of Public Statistical Data and the Geographic Model Transferability.38. International Conference on Information Systems (ICIS'17), Seoul: Südkorea, 10. – 13. Dezember
Hopf, K., Dagef?rde, F., Wolter, D. (2015). Identifying the Geographical Scope of Prohibition Signs, 12. International Conference on Spatial Information Theory (COSIT) , 2015 Santa Fe: NM, USA, 12. – 16. Oktober. Proceedings in Lecture Notes in Computer Science, DOI: 10.1007/978-3-319-23374-1_12
Hopf, K., Sodenkamp, M., Kozlovskiy, I., Staake, T. (2014). Feature extraction and filtering for household classification based on smart electricity meter data, 3. D-A-CH+ Energieinformatik Konferenz 2014, 13. -14. November. In: Computer Science - Research and Development 31 (3), pp. 141-148, DOI: 10.1007/s00450-014-0294-4
Software-Bibliotheken
Hopf, K., Weigert, A., Kozlovskiy, I., Staake, T. (2020). SmartMeterAnalytics: Methods for Smart Meter Data Analysis, Bibliothek für die Statistikumgebung GNU R, https://cran.r-project.org/package=SmartMeterAnalytics
Hopf, K., Weigert, A., Weinig, N., Staake, T., (2020). ResidentialEnergyConsumption: Residential Energy Consumption Data, Bibliothek für die Statistikumgebung GNU R, https://cran.r-project.org/package=ResidentialEnergyConsumption
Buchkapitel und Projektberichte
Weigert, A., Hopf, K., Staake, T., Rast, A., Marckhoff, J. (2020). SmartLoad – Smart Meter Data Analytics for Enhanced Energy Efficiency in the Residential Sector, Schlussbericht. Bundesamt für Energie, Schweiz (Online)
Hopf, K., Staake, T. (2019). Methoden der Energiedatenanalyse, Schlussbericht zum Eurostars Projekt ?Energy Data Analytics: Steigerung der Servicequalit?t und der Energieeffizienz im Privatkundenbereich“, DOI: 10.2314/KXP:1687331642
Sodenkamp, M., Hopf, K., Kozlovskiy, I., Staake, T. (2016). Smart-Meter-Datenanalyse für automatisierte Energieberatungen ("Smart Grid Data Analytics"), Schlussbericht. Bundesamt für Energie, Schweiz (Online)
Sodenkamp, M., Hopf, K., Staake, T. (2015). Using supervised machine learning to explore energy consumption data in private sector housing. In: Tavana, M. & Puranam, K. (Hg.): Handbook of Research on Organizational Transformations through Big Data Analytics. Hershey, USA: IGI Global, DOI: 10.4018/978-1-4666-7272-7.ch019