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Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece

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  • Ioannis-Panagiotis Raptis

    (Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, GR-15784 Athens, Greece)

  • Stelios Kazadzis

    (Physics and Meteorology Observatory of Davos, World Radiation Center (PMOD/WRC), CH-7260 Davos, Switzerland)

  • Ilias Fountoulakis

    (Research Centre for Atmospheric Physics and Climatology, Academy of Athens, GR-11527 Athens, Greece
    Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, GR-15236 Athens, Greece)

  • Kyriakoula Papachristopoulou

    (Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, GR-15784 Athens, Greece
    Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, GR-15236 Athens, Greece)

  • Dimitra Kouklaki

    (Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, GR-15784 Athens, Greece)

  • Basil E. Psiloglou

    (Institute for Environmental Research & Sustainable Development, National Observatory of Athens, GR-15236 Athens, Greece)

  • Andreas Kazantzidis

    (Physics Department, University of Patras, GR-26500 Patras, Greece)

  • Charilaos Benetatos

    (Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, GR-15784 Athens, Greece)

  • Nikolaos Papadimitriou

    (Physics Department, University of Patras, GR-26500 Patras, Greece)

  • Kostas Eleftheratos

    (Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, GR-15784 Athens, Greece
    Biomedical Research Foundation, Academy of Athens, GR-11527 Athens, Greece)

Abstract

Energy nowcasting is a valuable asset in managing energy loads and having real-time information on solar irradiation availability. In this study, we evaluate the spectrally integrated outputs of the SENSE system for solar irradiance nowcasting for the period of the ASPIRE (atmospheric parameters affecting spectral solar irradiance and solar energy) campaign (December 2020–December 2021) held in Athens, Greece. For the needs of the campaign, several ground-based instruments were operating, including two pyranometers, a pyrheliometer, a cloud camera, a CIMEL sunphotometer, and a precision spectral radiometer (PSR). Global horizontal irradiance (GHI) estimations were more accurate than direct normal irradiance (DNI). SENSE estimations are provided every 15 min, but when comparing bigger time intervals (hours-days), the statistics improved. A dedicated assessment of the SENSE’s inputs is performed in respect to ground-based retrievals, considering cloud conditions (from a sky imager), AOD, and precipitable water vapor from AERONET. The factor that established the larger errors was the visibility of the solar disc, which cannot be defined by the available sources of model inputs. Additionally, there were discrepancies between the satellite estimation of the clouds and the ground picture, which caused deviations in results. AOD differences affected more the DNI.

Suggested Citation

  • Ioannis-Panagiotis Raptis & Stelios Kazadzis & Ilias Fountoulakis & Kyriakoula Papachristopoulou & Dimitra Kouklaki & Basil E. Psiloglou & Andreas Kazantzidis & Charilaos Benetatos & Nikolaos Papadimi, 2023. "Evaluation of the Solar Energy Nowcasting System (SENSE) during a 12-Months Intensive Measurement Campaign in Athens, Greece," Energies, MDPI, vol. 16(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5361-:d:1193776
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    References listed on IDEAS

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    1. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    2. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    3. Yang, Dazhi & Wu, Elynn & Kleissl, Jan, 2019. "Operational solar forecasting for the real-time market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1499-1519.
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