  | ORTEGA MORENO, B.* , THARWAT, A.* , BURCHERI, L. M., DELGADO FERNANDEZ, J., & FRIDGEN, G. (2026). Privacy valuation and privacy-seeking behaviour: Lessons from an information treatment. In Proceedings of the 47th International Conference of the International Association of Energy Economists. International Association of Energy Economists (IAEE).  Peer reviewed* These authors have contributed equally to this work. |
 | SARTIPI, A., DELGADO FERNANDEZ, J., POTENCIANO MENCI, S., & MAGITTERI, A. (2025). Bridging Smart Meter Gaps: A Benchmark of Statistical, Machine Learning and Time Series Foundation Models for Data Imputation. In 2025 IEEE Kiel PowerTech (pp. 1-7). IEEE. doi:10.1109/PowerTech59965.2025.11180477 Peer reviewed |
 | NGUYEN, Q. V., DELGADO FERNANDEZ, J., & POTENCIANO MENCI, S. (2025). Spatiotemporal Graph Neural Networks for Short-Term Load Forecasting: Does a graph inferred from smart meter data help? In Spatiotemporal Graph Neural Networks for Short-Term Load Forecasting: Does a graph inferred from smart meter data help?Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PowerTech59965.2025.11180479 Peer reviewed |
 | DELGADO FERNANDEZ, J., POTENCIANO MENCI, S., & Magitteri, A. (2025). Forecasting Anonymized Electricity Load Profiles [Paper presentation]. PowerTech 2025, KIEL, Germany. doi:10.1109/PowerTech59965.2025.11180602 Peer reviewed |
 | ABELLÁN ÁLVAREZ, I., DELGADO FERNANDEZ, J., & POTENCIANO MENCI, S. (2025). Privacy-preserving distributed clustering: A fully homomorphic encrypted approach for time series. Computers and Security, 157, 104579. doi:10.1016/j.cose.2025.104579 Peer Reviewed verified by ORBi |
 | Radovanovic, D., DELGADO FERNANDEZ, J., Schirl, M., Eibl, G., Unterweger, A., & POTENCIANO MENCI, S. (2025). Inferring the Hidden: Privacy Risks of Microaggregation in Smart Meter Data [Paper presentation]. DACH+ Conference on Energy Informatics, Aachen, Germany. Peer reviewed |
 | Barbereau, T., DELGADO FERNANDEZ, J., & POTENCIANO MENCI, S. (2025). The governance of federated learning: a decision framework for organisational archetypes. Data and Policy, 7. doi:10.1017/dap.2025.10020 Peer reviewed |
 | NGUYEN, Q. V., POTENCIANO MENCI, S., & DELGADO FERNANDEZ, J. (2024). Literature review for large-scale load forecasting with large volume of smart-meter data. In Energy Proceedings vol 48. Sweden: Scanditale AB. doi:10.46855/energy-proceedings-11481 Peer reviewed |
 | Sündermann, J., DELGADO FERNANDEZ, J., Kellner, R., Doll, T., Froriep, U. P., & Bitsch, A. (2024). Medical device similarity analysis: a promising approach to medical device equivalence regulation. Expert Review of Medical Devices, 1 - 13. doi:10.1080/17434440.2024.2402027 Peer Reviewed verified by ORBi |
 | DELGADO FERNANDEZ, J., BARBEREAU, T. J., & PAPAGEORGIOU, O. (2024). Agent-based Model of Initial Token Allocations: Simulating Distributions post Fair Launch. ACM Transactions on Management Information Systems. doi:10.1145/3649318 Peer reviewed |
 | HORNEK, T., POTENCIANO MENCI, S., DELGADO FERNANDEZ, J., & PAVIĆ, I. (2024). Comparative Analysis of Baseline Models for Rolling Price Forecasts in the German Continuous Intraday Electricity Market. In Volume 38: Energy Transitions toward Carbon Neutrality: Part I. Stockholm, Sweden: Scanditale AB. doi:10.46855/energy-proceedings-10885 Peer reviewed |
 | Sprenkamp, K., DELGADO FERNANDEZ, J., Eckhardt, S., & Zavolokina, L. (2024). Overcoming intergovernmental data sharing challenges with federated learning. Data and Policy, 6 (27). doi:10.1017/dap.2024.19 Peer Reviewed verified by ORBi |
 | AMARD, A., DELGADO FERNANDEZ, J., BARBEREAU, T. J., FRIDGEN, G., & SEDLMEIR, J. (10 December 2023). Federated Learning in Migration Forecasting [Paper presentation]. ICIS 2023. Editorial reviewed |
 | DELGADO FERNANDEZ, J. (2023). Breaking data silos with Federated Learning [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/57042 |
  | DELGADO FERNANDEZ, J., POTENCIANO MENCI, S., & PAVIĆ, I. (2023). Towards a peer-to-peer residential short-term load forecasting with federated learning. In Proceedings of the 2023 IEEE Belgrade PowerTech (pp. 6). IEEE. doi:10.1109/PowerTech55446.2023.10202782 Peer reviewed |
 | LEE, C. M., DELGADO FERNANDEZ, J., POTENCIANO MENCI, S., RIEGER, A., & FRIDGEN, G. (03 January 2023). Federated Learning for Credit Risk Assessment [Paper presentation]. Proceedings of the 56th Hawaii International Conference on System Sciences, Maui, Hawaii, United States. Peer reviewed |
 | Sprenkamp, K., DELGADO FERNANDEZ, J., Eckhardt, S., & Zavolokina, L. (2023). Federated Learning as a Solution for Problems Related to Intergovernmental Data Sharing. In Proceedings of the 56th Hawaii International Conference on System Sciences (pp. 10). Peer reviewed |
 | DELGADO FERNANDEZ, J., POTENCIANO MENCI, S., LEE, C. M., RIEGER, A., & FRIDGEN, G. (15 November 2022). Privacy-preserving federated learning for residential short-term load forecasting. Applied Energy, 326. doi:10.1016/j.apenergy.2022.119915 Peer Reviewed verified by ORBi |
 | DELGADO FERNANDEZ, J., BARBEREAU, T. J., & PAPAGEORGIOU, O. (2022). Agent-based Model of Initial Token Allocations: Evaluating Wealth Concentration in Fair Launches. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/51934. doi:10.48550/arXiv.2208.10271 |
