IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5361-d1193776.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5361/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/14/5361/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    2. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. Mohanty, Sthitapragyan & Patra, Prashanta K. & Sahoo, Sudhansu S. & Mohanty, Asit, 2017. "Forecasting of solar energy with application for a growing economy like India: Survey and implication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 539-553.
    4. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    5. John Boland & Adrian Grantham, 2018. "Nonparametric Conditional Heteroscedastic Hourly Probabilistic Forecasting of Solar Radiation," J, MDPI, vol. 1(1), pages 1-18, December.
    6. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    7. Arumugham, Dinesh Rajan & Rajendran, Parvathy, 2021. "Modelling global solar irradiance for any location on earth through regression analysis using high-resolution data," Renewable Energy, Elsevier, vol. 180(C), pages 1114-1123.
    8. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    9. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    10. Liu, Bai & Yang, Dazhi & Mayer, Martin János & Coimbra, Carlos F.M. & Kleissl, Jan & Kay, Merlinde & Wang, Wenting & Bright, Jamie M. & Xia, Xiang’ao & Lv, Xin & Srinivasan, Dipti & Wu, Yan & Beyer, H, 2023. "Predictability and forecast skill of solar irradiance over the contiguous United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    11. Ying-Yi Hong & Ti-Hsuan Yu & Ching-Yun Liu, 2013. "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition," Energies, MDPI, vol. 6(12), pages 1-16, November.
    12. Eduardo Rangel-Heras & César Angeles-Camacho & Erasmo Cadenas-Calderón & Rafael Campos-Amezcua, 2022. "Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model," Energies, MDPI, vol. 15(8), pages 1-23, April.
    13. Pronti, A. & Zoboli, R., 2024. "Something new under the sun. A spatial econometric analysis of the adoption of photovoltaic systems in Italy," Energy Economics, Elsevier, vol. 134(C).
    14. Visser, Lennard & AlSkaif, Tarek & van Sark, Wilfried, 2022. "Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution," Renewable Energy, Elsevier, vol. 183(C), pages 267-282.
    15. Kihan Kim & Jin Hur, 2019. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources," Energies, MDPI, vol. 12(17), pages 1-13, August.
    16. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    17. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    18. Botman, Lola & Lago, Jesus & Fu, Xiaohan & Chia, Keaton & Wolf, Jesse & Kleissl, Jan & De Moor, Bart, 2024. "Building plug load mode detection, forecasting and scheduling," Applied Energy, Elsevier, vol. 364(C).
    19. Si-Ya Wang & Jun Qiu & Fang-Fang Li, 2018. "Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records," Energies, MDPI, vol. 11(6), pages 1-17, May.
    20. Chao-Rong Chen & Unit Three Kartini, 2017. "k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data," Energies, MDPI, vol. 10(2), pages 1-18, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5361-:d:1193776. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.