IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v72y2014icp291-300.html
   My bibliography  Save this article

Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model

Author

Listed:
  • Dambreville, Romain
  • Blanc, Philippe
  • Chanussot, Jocelyn
  • Boldo, Didier

Abstract

The integration of massive solar energy supply in the existing grids requires an accurate forecast of the solar resources to manage the energetic balance. In this context, we propose a new approach to forecast the Global Horizontal Irradiance at ground level from satellite images and ground based measurements. The training of spatio-temporal multidimensional autoregressive models with HelioClim-3 data along with 15-min averaged GHI times series is tested with respect to a ground based station from the BSRN network. Forecast horizons from 15 min to 1 h provided very promising results validated on a one year ground-based pyranometric data set. The performances have been compared to another similar method from the literature by means of relative metrics. The proposed approach paves the way of the use of satellite-based surface solar irradiance (SSI) estimation as an SSI map nowcasting method that enables to capture spatio-temporal correlation for the improvement of a local SSI forecast.

Suggested Citation

  • Dambreville, Romain & Blanc, Philippe & Chanussot, Jocelyn & Boldo, Didier, 2014. "Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model," Renewable Energy, Elsevier, vol. 72(C), pages 291-300.
  • Handle: RePEc:eee:renene:v:72:y:2014:i:c:p:291-300
    DOI: 10.1016/j.renene.2014.07.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014811400398X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2014.07.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    2. Escrig, H. & Batlles, F.J. & Alonso, J. & Baena, F.M. & Bosch, J.L. & Salbidegoitia, I.B. & Burgaleta, J.I., 2013. "Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast," Energy, Elsevier, vol. 55(C), pages 853-859.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
    2. André, Maïna & Dabo-Niang, Sophie & Soubdhan, Ted & Ould-Baba, Hanany, 2016. "Predictive spatio-temporal model for spatially sparse global solar radiation data," Energy, Elsevier, vol. 111(C), pages 599-608.
    3. Aguiar, L. Mazorra & Pereira, B. & Lauret, P. & Díaz, F. & David, M., 2016. "Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting," Renewable Energy, Elsevier, vol. 97(C), pages 599-610.
    4. Gianfranco Chicco & Valeria Cocina & Paolo Di Leo & Filippo Spertino & Alessandro Massi Pavan, 2015. "Error Assessment of Solar Irradiance Forecasts and AC Power from Energy Conversion Model in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(1), pages 1-27, December.
    5. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    6. Alonso-Suárez, R. & David, M. & Branco, V. & Lauret, P., 2020. "Intra-day solar probabilistic forecasts including local short-term variability and satellite information," Renewable Energy, Elsevier, vol. 158(C), pages 554-573.
    7. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    8. Elena Collino & Dario Ronzio, 2021. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System," Energies, MDPI, vol. 14(3), pages 1-30, February.
    9. Lan, Hai & Yin, He & Hong, Ying-Yi & Wen, Shuli & Yu, David C. & Cheng, Peng, 2018. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route," Applied Energy, Elsevier, vol. 211(C), pages 15-27.
    10. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.
    11. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    12. Stéphanie Monjoly & Maina André & Rudy Calif & Ted Soubdhan, 2019. "Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model," Energies, MDPI, vol. 12(12), pages 1-20, June.
    13. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    14. Xwégnon Ghislain Agoua & Robin Girard & Georges Kariniotakis, 2021. "Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy," Energies, MDPI, vol. 14(5), pages 1-15, March.
    15. Pramesti Getut, 2023. "Parameter least-squares estimation for time-inhomogeneous Ornstein–Uhlenbeck process," Monte Carlo Methods and Applications, De Gruyter, vol. 29(1), pages 1-32, March.
    16. Severiano, Carlos A. & Silva, Petrônio Cândido de Lima e & Weiss Cohen, Miri & Guimarães, Frederico Gadelha, 2021. "Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems," Renewable Energy, Elsevier, vol. 171(C), pages 764-783.
    17. Paletta, Quentin & Arbod, Guillaume & Lasenby, Joan, 2023. "Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions," Applied Energy, Elsevier, vol. 336(C).
    18. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    19. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    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.
    21. 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).
    22. Monjoly, Stéphanie & André, Maïna & Calif, Rudy & Soubdhan, Ted, 2017. "Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach," Energy, Elsevier, vol. 119(C), pages 288-298.
    23. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
    24. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).

    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. Kathryn M. Dominguez, 1991. "Do Exchange Auctions Work? An Examination of the Bolivian Experience," NBER Working Papers 3683, National Bureau of Economic Research, Inc.
    2. Jacint Balaguer & Manuel Cantavella-Jorda, 2004. "Structural change in exports and economic growth: cointegration and causality analysis for Spain (1961-2000)," Applied Economics, Taylor & Francis Journals, vol. 36(5), pages 473-477.
    3. Muhammad Farooq Arby & Amjad Ali, 2017. "Threshold Inflation in Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 13, pages 1-19.
    4. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Ramona Dumitriu & Razvan Stefanescu, 2015. "The Relationship Between Romanian Exports And Economic Growth After The Adhesion To European Union," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 17-26.
    6. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages C32-C61, 03.

    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:eee:renene:v:72:y:2014:i:c:p:291-300. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.