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Angela Meyer

Professor and Research Group Leader

Contact: email_at_angela-meyer.net

Bern University of Applied Sciences

School of Engineering and Computer Science

Quellgasse 21

2501 Biel

Switzerland

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 We have two open positions for early-career researchers in physics-informed and transfer learning for intelligent renewable energy systems. Click here and here to submit your application.

Research

I am a Professor of Applied Machine Learning at the School of Engineering and Computer Science at Bern University of Applied Sciences. My research aims at developing intelligent decision support systems to increase the resilience and sustainability of infrastructure, 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. My research group is supported by the Swiss Innovation Agency Innosuisse and the Swiss National Science Foundation.

News

Nov 28, 2022: We submitted our study L. Jenkel, S. Jonas, A. Meyer, 2022, Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning.

Nov 18, 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.

Nov 15, 2022: Our study A. Carpentieri, D. Folini, M. Wild, A. Meyer, 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" on 9 Dec. 2022

Sept 16, 2022: We submitted our paper A. Carpentieri, D. Folini, M. Wild, L. Vuilleumier, A. Meyer, 2022, Satellite-derived solar radiation for intra-hour and intra-day applications: Biases and uncertainties by season and altitude. [Link]

Sept 13, 2022: We are hiring a second postdoc researcher in transfer learning and domain adaptation for applications in renewable energy and industrial fleets. Apply here.  

Sept 1, 2022: We are hiring a postdoctoral researcher in physics-informed machine learning and 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.

July 15, 2022: Submitted our paper S. Jonas, D. Anagnostos, B. Brodbeck, A. Meyer, 2022, Vibration fault detection in wind turbines based on normal behaviour models without feature engineering. [Link]

June 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.

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.

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.

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]

July 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]

June 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]

June 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 has been approved and 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 (PIPP) has been approved and is being funded by the Swiss National Science Foundation. The PIPP project aims at developing more accurate forecasting methods of the photovoltaic power generation at lead times of up to several hours. I am hiring a PhD student for this project. We look forward to receiving your online application via the BFH website.

Feb. 28, 2021: Our paper J. Maron, D. Anagnostos, B. Brodbeck, A. Meyer, Gear bearing fault detection in wind turbines with multi-target neural networks, will be presented at WESC 2021 in May.

Dec. 7, 2020: Submitted our paper A. Meyer, 2020, Multi-target normal behaviour models for wind farm condition monitoring [arXiv]

Sept. 9, 2020: Organising a mini-symposium on Data-driven technologies for O&M cost reduction at the Wind Energy Science conference 2021 (WESC 2021) with Dr Ravi Pandit (University of Exeter). Call for abstracts is open!

June 15, 2020: Accepted! Our paper: A. Meyer, B. Brodbeck, Data-driven Performance Fault Detection in Commercial Wind Turbines, has been accepted for presentation at the 5th European Conference of the Prognostics and Health Management Society (PHME20).

May 13, 2020: Accepted! Our paper: L. Vuilleumier, A. Meyer, R. Stöckli, S. Wilbert, L. Zarzalejo, Accuracy of satellite-derived Solar Direct Irradiance in Southern Spain and Switzerland, has been accepted for publication in the International Journal of Remote Sensing

May 5, 2020: Presented our work on performance-related fault detection in wind turbines at the Prognostics and Health Management conference PHM2020, Besancon.

Apr. 30, 2020: Submitted our paper A. Meyer, B. Brodbeck, 2020, Performance Fault Detection in Wind Turbines by Dynamic Reference State Estimation [arXiv]

Peer-reviewed Publications

 

In Review

  • Jenkel, L., S. Jonas, A. Meyer: Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning.

  • 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. [Link]

  • Jonas, S., D. Anagnostos, B. Brodbeck, A. Meyer: Vibration fault detection in wind turbines based on normal behaviour models without feature engineering. [Link]

  • Lu, N., F. Bilendo, A. Meyer, H. Badihi, P. Cambron, B. Jiang: Applications and Modeling Techniques of Wind Turbine Power Curve for Onshore and Offshore Wind Farms - A Review.

2023

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

2022

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

  • 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. [Link]

2021

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

  • Meyer, A. (2021): 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.

2020

  • Meyer, A., 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, Download.

  • 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 Sensing, doi: 10.1080/01431161.2020.1783712. *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 (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, A. Meyer, L. Vuilleumier, D. Heinemann, P. Blanc, R. Pitz‐Paal (2018): 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.

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 (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. [Article] *shared first authorship

  • Meyer, A., L. Vuilleumier, R. Stöckli, S. Wilbert, L. Zarzalejo (2017): Validation of Direct Normal Irradiance from Meteosat Second Generation, Conference Paper presented at European Meteorological Society Annual Meeting 2017, EMS2017-365-1.

  • 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.

2016

  • Meyer, A., L. Vuilleumier, R. Stöckli, S. Wilbert, L. Zarzalejo (2016): Validation of Direct Normal Irradiance from Meteosat Second Generation, Conference Paper presented at European Geosciences Union General Assembly 2016, EPSC2016-14541.

  • 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. [Article]

2015

  • 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. [Article]