Prof. Dr. Angela Meyer

Data Science

Industry 4.0 and Artificial Intelligence for Smart Production

Research project "Artificial Intelligence for Improving the Reliability and Resilience of Industrial Fleets", grant by the Swiss National Science Foundation

Research project "Probabilistic Intraday Forecasting of Photovoltaic Power Generation for the Swiss Plateau", grant by the Swiss National Science Foundation

Research project "Real-time Production and Maintenance Planning: New Services to Increase the Revenues of Wind Farms", grant by the Swiss Innovation Agency Innosuisse

COST Action CA20109 “Modular Energy Islands for Sustainability and Resilience”

Carpentieri, A., S. Pulkkinen, D. Nerini, D. Folini, M. Wild, A. Meyer (2023): Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection, Applied Energy, 351, 2023, doi: 10.1016/j.apenergy.2023.121775

Jenkel, L., S. Jonas, A. Meyer (2023): Privacy-preserving Fleet-wide Learning of Wind Turbine Conditions with Federated Learning, Energies, 16(17), doi:10.3390/en16176377.

Carpentieri, A., D. Folini, M. Wild, L. Vuilleumier, A. Meyer (2023): Satellite-derived solar radiation for intra-hour and intra-day applications: Biases and uncertainties by season and altitude, Solar Energy, 255, 274-284, doi: 10.1016/j.solener.2023.03.027.

Jonas, S., D. Anagnostos, B. Brodbeck, A. Meyer (2023): Vibration fault detection in wind turbines based on normal behaviour models without feature engineering, Energies, 16(4), 1760, doi: 10.3390/en16041760.

Bilendo, F., N. Lu, H. Badihi, A. Meyer, U. Cali, P. Cambron (2023): Multi-Target Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring, IEEE Transactions on Industrial Informatics, accepted for publication.

Meyer, A. (2023): SCADA-based fault detection in wind turbines: Data-driven techniques and applications, In: Non-Destructive Testing and Condition Monitoring Techniques In Wind Energy, Academic Press, Editors: F. Marquez, M. Papaelias, V. Jantara Junior, ISBN 9780323996662.

Bilendo, F., A. Meyer, H. Badihi, N. Lu, P. Cambron, B. Jiang (2023): Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms - A Review, Energies, 16(1), 180, doi:10.3390/en16010180.

Maron, J., D. Anagnostos, B. Brodbeck, A. Meyer (2022): Artificial intelligence-based condition monitoring and predictive maintenance framework for wind turbines, Journal of Physics Conference Series, doi: 10.1088/1742-6596/2151/1/012007.

Meyer, A. (2022): Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning, Energies, doi: 10.3390/en15041514.

Carpentieri, A., M. Wild, D. Folini, A. Meyer (2022): Characterizing and correcting Heliosat Surface Solar Radiation bias on intra-day time scales with deep neural networks, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-366, doi:10.5194/ems2022-366.

Carpentieri, A., M. Wild, D. Folini, A. Meyer (2022): Deep learning for improved bias correction of satellite-derived SIS maps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3641, doi:10.5194/egusphere-egu22-3641.

Jonas, S., D. Anagnostos, B. Brodbeck, A. Meyer (2022): Vibration fault detection in wind turbines based on normal behaviour models without feature engineering, https://arxiv.org/abs/2206.12452.

Meyer, A. (2021): Multi-target normal behaviour models for wind farm condition monitoring, Applied Energy, Elsevier, doi: 10.1016/j.apenergy.2021.117342

Meyer, A. (2021): Early fault detection with multi-target neural networks, Lecture Notes in Computer Science, Springer, doi: 10.1007/978-3-030-86970-0_30.

Meyer, A. and B. Brodbeck (2020): Data-driven Performance Fault Detection in Commercial Wind Turbines, Proceedings of the 5th European Conference of the Prognostics and Health Management Society (PHME20), ISBN 978-1-93-626332-5.

Vuilleumier, L.*, A. Meyer*, R. Stöckli, S. Wilbert, L. Zarzalejo (2020): Accuracy of Satellite-derived Solar Direct Irradiance in Southern Spain and Switzerland, International Journal of Remote, doi: 10.1080/01431161.2020.1783712. *shared first authorship

Kuhn, P., S. Wilbert, C. Prahl, D. Garsche, D. Schüler, T. Haase, L. Ramirez, L. Zarzalejo, A. Meyer, P. Blanc, R. Pitz-Paal (2018): Applications of a shadow camera system for energy meteorology, Advances in Science and Research, doi: 10.5194/asr-15-11-2018.

Kuhn, P., B. Nouri, S. Wilbert, C. Prahl, N. Kozonek, T. Schmidt, Z. Yasser, L. Ramirez, L. Zarzalejo, , L. Vuilleumier, D. Heinemann, P. Blanc, R. Pitz‐Paal (2017): Validation of an all‐sky imager–based nowcasting system for industrial PV plants, Progress in Photovoltaics: Research and Applications, 26, doi: 10.1002/pip.2968.

Kuhn, P., S. Wilbert, C. Prahl, D. Schüler, T. Haase, T. Hirsch, M. Wittmann, L. Ramirez, L. Zarzalejo, A. Meyer, L. Vuilleumier, P. Blanc, R. Pitz-Paal (2017): Shadow camera system for the generation of solar irradiance maps, Solar Energy, doi: 10.1016/j.solener.2017.05.074.

Gasparini, B.*, A. Meyer*, D. Neubauer, S. Münch, U. Lohmann (2017): Cirrus cloud properties as seen by the CALIPSO satellite and ECHAM-HAM global climate model, Journal of Climate, doi: 10.1175/JCLI-D-16-0608.1. *shared first authorship

Kuhn, P., S. Wilbert, D. Schüler, C. Prahl, T. Haase, L. Ramirez, L. Zarzalejo, A. Meyer, L. Vuilleumier, P. Blanc, J. Dubrana, A. Kazantzidis, M. Schroedter-Homscheidt, T. Hirsch, R. Pitz-Paal (2017): Validation of spatially resolved all sky imager derived DNI nowcasts, AIP Conference Proceedings, doi: 10.1063/1.4984522.

Meyer, A., D. Folini, U. Lohmann, and T. Peter (2016): Tropical temperature and precipitation responses to large volcanic eruptions: Observations and AMIP5 simulations, Journal of Climate, doi: 10.1175/JCLI-D-15-0034.1.

Meyer, A., J.-P. Vernier, B. Luo, U. Lohmann, and T. Peter (2015): Did the 2011 Nabro eruption affect the optical properties of ice clouds?, J. Geophys. Res. Atmos., 120, doi: 10.1002/2015JD023326.