Using a Multi-view Convolutional Neural Network to monitor solar irradiance Articles uri icon

publication date

  • April 2021

start page

  • 1

end page

  • 13

International Standard Serial Number (ISSN)

  • 0941-0643

Electronic International Standard Serial Number (EISSN)

  • 1433-3058

abstract

  • In the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global
    horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the massive deployment of this energy. Thermopile
    or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is
    important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural
    networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional
    neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This
    neural network is capable of combining images from three cameras. Results show that this approach is more accurate than
    using only images from a single camera. It has also been shown to improve the performance of two other approaches: a
    cloud fraction model and a feature extraction model.

keywords

  • deep learning; multi-view image; solar irradiance