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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

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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Our group has a new PhD vacancy in solar energy forecasting coming up. Drop me a message with your full CV, motivation letter, graduation certificates, Github profile if you are interested. 

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 of six PhD and postdoctoral researchers is supported by the Swiss Innovation Agency Innosuisse and the Swiss 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]

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]

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].

Dec 4, 2023: We welcome Ana-Christina Marza who joins our group as a doctoral researcher in subseasonal forecasting for energy applications.

Oct 26, 2023: Our study A. Carpentieri, D. Folini, M. Wild, A. Meyer, 2023, The challenge of intermittency: Probabilistic spatiotemporal forecasting of photovoltaic energy production will be presented in a talk at AMS Annual Meeting in Baltimore, USA on 31 Jan. 2024.

Oct 23, 2023: Accepted! Our paper F. Bilendo, N. Lu, H. Badihi, A. Meyer, U. Cali, P. Cambron, Multi-Target Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring has been accepted for publication in IEEE Transactions on Industrial Informatics.

Oct 16, 2023: We welcome Kevin Schuurman who joins our group as a doctoral researcher in solar energy forecasting.

Sep 1, 2023: Accepted! Our paper L. Jenkel, S. Jonas, A. Meyer, Privacy-preserving Fleet-wide Learning of Wind Turbine Conditions with Federated Learning has been accepted for publication in Energies. [Link]

Aug 17, 2023: Accepted! Our paper A. Carpentieri, S. Pulkkinen, D. Nerini, D. Folini, M. Wild, A. Meyer, Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection has been accepted for publication in Applied Energy. [Link] [PDF]

Jun 30, 2023: Our study A. Carpentieri, D. Folini, M. Wild, A. Meyer, Scale-dependent Temporal Variability of the Clear-Sky Index and its Relevance for Solar Radiation Forecasts, will be presented in a talk at EMS in Bratislava, Slovakia on 4 Sept. 2023.

Jun 16, 2023: We welcome Dr. Albin Grataloup who joins our group as a postdoctoral researcher in renewable energy applications.

Apr 3, 2023: Our study Jenkel et al., Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning, will be presented at WindEurope in Lyon, France in June. [Link]

Mar 15, 2023: Accepted! Our paper A. Carpentieri, 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 has been accepted for publication in Solar Energy [Link]

Feb 25, 2023: Our studies Jenkel et al., Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning, and Jonas et al., Vibration fault detection in wind turbines based on normal behaviour models without feature engineering will be presented in talks at WESC in Glasgow in May. [Link] [Link]

Feb 23, 2023: Our study A. Carpentieri, D. Folini, M. Wild, A. Meyer, Short-term probabilistic forecast of cloudiness: a scale-dependent advection approach, will be presented in a talk at EGU in Vienna on 28 April.

Feb 16, 2023: Honored to be nominated for AcademiaNet by the Swiss National Science Foundation.

Feb 10, 2023: Accepted! Our paper S. Jonas, D. Anagnostos, B. Brodbeck, A. Meyer, Vibration fault detection in wind turbines based on normal behaviour models without feature engineering, has been accepted for publication in Energies.

Feb 8, 2023: Stefan Jonas joins our team as a PhD candidate! Stefan will be working on transfer learning in renewable energy applications.

Jan 26, 2023: Our work A. Carpentieri, D. Folini, M. Wild, A. Meyer, Underestimation of satellite-based surface solar radiation in the Swiss Alps: a bias correction approach, will be presented at the Alpenforce Energy Research Talks in Disentis, Switzerland, today.

Dec 20, 2022: Accepted! Our paper F. Bilendo, A. Meyer, H. Badihi, N. Lu, P. Cambron, B. Jiang, Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms - A Review, has been accepted for publication in Energies.

Dec 9, 2022: Our study A. Carpentieri, D. Folini, M. Wild, A. Meyer, 2022, Probabilistic forecasting of regional photovoltaic power production based on satellite-derived cloud motion, will be presented at the NeurIPS 2022 workshop "Tackling Climate Change with Machine Learning" today.

Nov 13, 2022: I am giving a presentation on Artificial Intelligence in Predictive Maintenance at the Industry 4.0 conference of SwissMEM in Lucerne, Switzerland, on 24 January 2023.

Sep 1, 2022: We are hiring two postdoctoral researchers in intelligent maintenance of renewable power systems. Click here to apply.

Aug 19, 2022: I am giving an invited talk on AI for predictive maintenance of wind farms at the RISE Research Institutes of Sweden, Gothenburg, on 12 Sept. 2022. [Link]

Jun 28, 2022: Our study A. Carpentieri, M. Wild, D. Folini, A. Meyer, Characterizing and correcting Heliosat Surface Solar Radiation bias on intraday time scales with deep neural networks, has been accepted for oral presentation at the EMS conference in Bonn, Germany, on 6 Sept. 2022. [Link]

May 6, 2022: Our study A. Meyer, Vibration fault diagnosis in wind turbine gearboxes with automated feature learning, has been accepted for oral presentation at the WindEurope Technology Workshop 2022 in Brussels, Belgium. [Link]

Apr 30, 2022: I am giving an invited talk on Opportunities and challenges in PHM of wind farms at the PHM conference in London, UK, on 30 May 2022.

Mar 2, 2022: Our group has been awarded a three-year research grant by the Swiss National Science Foundation for our project Artificial Intelligence for Improving the Reliability and Resilience of Industrial Fleets.

Mar 1, 2022: Our study A. Carpentieri, M. Wild, D. Folini, A. Meyer, Deep learning for improved bias correction of satellite-derived Surface Incoming Solar radiation maps, has been accepted for oral presentation at the EGU General Assembly 2022 in Vienna, Austria, on 25 May 2022. [Link]

Feb 25, 2022: I am honored to join the Young Editorial Board of the Elsevier journal Advances in Applied Energy. The journal publishes high-impact applied research in energy innovation and future energy transition topics.

Feb 24, 2022: Accepted! Our paper A. Meyer, Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning, has been accepted for publication in Energies. [Link]

Jan 3, 2022: Alberto Carpentieri is joining my team as a PhD student. He will be working at the interface of deep learning and probabilistic short-term forecasting of solar power generation. Welcome, Alberto! 

Nov 20, 2021: Accepted! Our article J. Maron, D. Anagnostos, B. Brodbeck, A. Meyer, Artificial intelligence-based condition monitoring and predictive maintenance framework for wind turbines, has been accepted for publication in Journal of Physics Conference Series.

Nov 10, 2021: We have an open position for a research assistant in machine learning, intelligent predictive maintenance and forecasting. Click here to submit your application.

Aug 11, 2021: We have a vacancy for a PhD position to develop a machine learning framework for short-term predictions of solar resource and photovoltaic power generation (BFH / ETH Zurich). Please upload your application documents through our webpage [Link]

Jul 13, 2021: Accepted! Our paper A. Meyer, Multi-target normal behaviour models for wind farm condition monitoring, has been accepted and published in Applied Energy. [Link]

Jun 10, 2021: Accepted! Our paper A. Meyer, Early fault detection with multi-target neural networks, has been accepted for publication in Lecture Notes in Computer Science and for presentation at ICCSA 2021. [preprint]

Jun 9, 2021: Our abstract J. Maron, D. Anagnostos, B. Brodbeck, A. Meyer, AI-based condition monitoring and predictive maintenance framework for wind turbines, has been accepted for presentation at WindEurope Electric City in Copenhagen, Denmark in November 2021.

May 1, 2021: Our joint research project with a Swiss SME on Predictive Maintenance for Wind Turbines is being funded by the Swiss Innovation Agency, Innosuisse. The project aims at developing fault detection algorithms for wind turbine components. We are looking forward to this exciting collaboration!

Apr 17, 2021: Our project proposal Probabilistic Intraday Forecasting of Photovoltaic Power generation for the Swiss Plateau is being funded by the Swiss National Science Foundation. The project aims at developing more accurate forecasting methods of the photovoltaic power generation at lead times of up to several hours.

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, accepted, [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, accepted [Link to preprint]

  • 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]

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]

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]

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.

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

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.

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.

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]

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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