Prof. Dr. Angela Meyer

Profile

Prof. Dr. Angela Meyer Dozentin

  • Address Berner Fachhochschule
    School of Engineering and Computer Science
    Lehre
    Quellgasse 21
    2501 Biel

Activities

  • Young Editorial Board member for Advances in Applied Energy (ISSN 2666-7924)

  • Reviewer for Applied Energy (ISSN 0306-2619), Energies (ISSN 1996-1073) and Electronics (ISSN 2079-9292)

  • Program committee member at ECML PKDD 2021, Bilbao, Spain, and at LOD 2021, Grasmere, United Kingdom

  • Management committee member of COST Action CA20109 “Modular Energy Islands for Sustainability and Resilience”

  • Session chair at the Wind Energy Science conference 2021, Hanover, Germany, session on "Data-driven technologies for O&M cost reduction”

Teaching

  • Data Science

  • Industry 4.0 and Artificial Intelligence for Smart Production

CV

  • 2015 Doctorate ETH Zurich
  • 2009 Master in mathematics University of Cambridge

Projects

  • 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”

Publications

  • 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.

  • 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.