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    Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking

    Barbieri F 2019.pdf (11.38Mb)
    Access Status
    Open access
    Authors
    Barbieri, Florian Benjamin Eric
    Date
    2019
    Supervisor
    Arindam Ghosh
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing, and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/77126
    Collection
    • Curtin Theses
    Abstract

    Tracking clouds with a sky camera within a very short horizon below thirty seconds can be a solution to mitigate the effects of sunlight disruptions. A Probability Hypothesis Density (PHD) filter and a Cardinalised Probability Hypothesis Density (CPHD) filter were used on a set of pre-processed sky images. Both filters have been compared with the state-of-the-art methods for performance. It was found that both filters are suitable to perform very-short term irradiance forecasting.

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