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Angela Meyer

Assistant Professor at TU Delft and BFH

Bern University of Applied Sciences

School of Engineering and Computer Science

Quellgasse 21, CH-2501 Biel

angela.meyer@bfh.ch

Phone: +41 32 321 64 69

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Delft University of Technology

Department of Geoscience and Remote Sensing

Stevinweg 1, NL-2628 CN Delft

angela.meyer@tudelft.nl

Phone: +31 15 278 8392

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We are hiring!

Our group has 4 new PhD positions and a postdoc position coming up in new projects in data assimilation, emulation, and forecasting for energy applications. Drop me a message with your CV, graduation certificates and references if you are interested. 

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Research

I am an Assistant Professor of Energy Meteorology at TU Delft and a Professor of Applied Machine Learning at BFH. My research aims at developing intelligent decision support systems to increase the resilience and sustainability of industrial and energy systems with sensor-driven and machine learning approaches. Before joining BFH, I was a doctoral and postdoctoral researcher at ETH Zurich, developed machine learning and predictive maintenance applications at the R&D centre of Hexagon AB, and led a remote condition monitoring R&D program at Siemens Smart Infrastructure. Our research group is supported by the European Commission, the Swiss Innovation Agency Innosuisse and National Science Foundation.

News

May 15, 2024: Accepted! Our paper A. Grataloup, S. Jonas, A. Meyer, A review of federated learning in renewable energy applications: Potential, challenges, and future directions has been accepted for publication in Energy and AI. [Link]

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Mar 14, 2024: Our study K. Schuurman, A. Meyer, Predicting surface solar irradiance from satellite imagery with deep learning radiative transfer emulation, will be presented at EGU24 in Vienna in April [Link], and so will be our paper Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, SHADECast: Enhancing solar energy integration through probabilistic regional forecasts [Link]

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Dec 22, 2023: Excited to share our new papers: Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, Extending intraday solar forecast horizons with deep generative models, arXiv:2312.11966 [Link], and Grataloup, A., S. Jonas, A. Meyer, A review of federated learning in renewable energy applications: Potential, challenges, and future directions, arXiv:2312.11220 [Link].

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Preprints

  • Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, Extending intraday solar forecast horizons with deep generative models, arXiv:2312.11966 [Link] 

Peer-reviewed Publications

2024

  • Grataloup, A., S. Jonas, A. Meyer, A review of federated learning in renewable energy applications:  Potential, challenges, and future directions, Energy and AI, 17, doi:10.1016/j.egyai.2024.100375, [Link]

  • Jonas, S., K. Winter, B. Brodbeck, A. Meyer, 2024, Bias correction of wind power forecasts with SCADA data and continuous learning, Journal of Physics: Conference Series, doi:10.1088/1742-6596/2767/9/092061 [Link]

  • Bilendo, F., N. Lu, H. Badihi, A. Meyer, U. Cali, P. Cambron, 2024, Multi-Target Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring, IEEE Transactions on Industrial Informatics, 20, 4, doi:10.1109/TII.2023.3331766 [Link]

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2023

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

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

  • Meyer, A., 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, doi: 10.1016/B978-0-323-99666-2.00001-0, 2023. [Link]

  • Carpentieri, A., D. Folini, M. Wild, L. Vuilleumier, A. Meyer, 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, 2023. [Link]

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

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2022

  • Bilendo, F., A. Meyer, H. Badihi, N. Lu, P. Cambron, B. Jiang, Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms - A Review, Energies, 16(1), 180, doi:10.3390/en16010180, 2022.

  • Meyer, A., Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning, Energies, 15(4), doi: 10.3390/en15041514, 2022. [Link]

  • Maron, J., D. Anagnostos, B. Brodbeck, A. Meyer, 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, 2022. [Link]

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2021

  • Meyer, A., Multi-target normal behaviour models for wind farm condition monitoring, Applied Energy, doi: 10.1016/j.apenergy.2021.117342, 2021. [Link] [Article]

  • Meyer, A., Early fault detection with multi-target neural networks, Lecture Notes in Computer Science, Vol. 12953, Springer, in: O. Gervasi et al. (Eds.): ICCSA 2021, LNCS 12951, pp. 1–9, 2021, doi: 10.1007/978-3-030-86970-0_30, 2021.

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2020

  • Meyer, A., B. Brodbeck, 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, 2020. [Download]​

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

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2018

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

  • Kuhn, P., B. Nouri, S. Wilbert, C. Prahl, N. Kozonek, T. Schmidt, Z. Yasser, L. Ramirez, L. Zarzalejo, A. Meyer, L. Vuilleumier, D. Heinemann, P. Blanc, R. Pitz‐Paal, 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, 2018.

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2017

  • 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, Shadow camera system for the generation of solar irradiance maps, Solar Energy, doi: 10.1016/j.solener.2017.05.074, 2017.

  • Gasparini, B.*, A. Meyer*, D. Neubauer, S. Münch, U. Lohmann, 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, 2017. [Article] *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, Validation of spatially resolved all sky imager derived DNI nowcasts, AIP Conference Proceedings, doi: 10.1063/1.4984522, 2017.

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2016

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

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2015

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

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