IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3518-d1126568.html
   My bibliography  Save this article

Evaluation of High Resolution WRF Solar

Author

Listed:
  • Jayesh Thaker

    (Department of Physics, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany)

  • Robert Höller

    (School of Engineering, University of Applied Sciences Upper Austria, Stelzhamerstrasse 23, 4600 Wels, Austria)

Abstract

The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS.

Suggested Citation

  • Jayesh Thaker & Robert Höller, 2023. "Evaluation of High Resolution WRF Solar," Energies, MDPI, vol. 16(8), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3518-:d:1126568
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3518/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/8/3518/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gandhi, Oktoviano & Zhang, Wenjie & Rodríguez-Gallegos, Carlos D. & Verbois, Hadrien & Sun, Hongbin & Reindl, Thomas & Srinivasan, Dipti, 2020. "Local reactive power dispatch optimisation minimising global objectives," Applied Energy, Elsevier, vol. 262(C).
    2. Kaur, Amanpreet & Nonnenmacher, Lukas & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2016. "Benefits of solar forecasting for energy imbalance markets," Renewable Energy, Elsevier, vol. 86(C), pages 819-830.
    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. Rodríguez-Gallegos, Carlos D. & Vinayagam, Lokesh & Gandhi, Oktoviano & Yagli, Gokhan Mert & Alvarez-Alvarado, Manuel S. & Srinivasan, Dipti & Reindl, Thomas & Panda, S.K., 2021. "Novel forecast-based dispatch strategy optimization for PV hybrid systems in real time," Energy, Elsevier, vol. 222(C).
    2. A.S. Jameel Hassan & Umar Marikkar & G.W. Kasun Prabhath & Aranee Balachandran & W.G. Chaminda Bandara & Parakrama B. Ekanayake & Roshan I. Godaliyadda & Janaka B. Ekanayake, 2021. "A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration," Energies, MDPI, vol. 14(20), pages 1-24, October.
    3. Zhou, Yu & Li, Zhengshuo & Wang, Guangrui, 2021. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization," Applied Energy, Elsevier, vol. 298(C).
    4. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    5. 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).
    6. 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).
    7. Alessandro Niccolai & Seyedamir Orooji & Andrea Matteri & Emanuele Ogliari & Sonia Leva, 2022. "Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images," Forecasting, MDPI, vol. 4(1), pages 1-11, March.
    8. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    9. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    10. Anaya, Karim L. & Pollitt, Michael G., 2022. "A social cost benefit analysis for the procurement of reactive power: The case of Power Potential," Applied Energy, Elsevier, vol. 312(C).
    11. Carpentieri, A. & Folini, D. & Nerini, D. & Pulkkinen, S. & Wild, M. & Meyer, A., 2023. "Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection," Applied Energy, Elsevier, vol. 351(C).
    12. Hugo Bezerra Menezes Leite & Hamidreza Zareipour, 2023. "Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites," Energies, MDPI, vol. 16(3), pages 1-14, February.
    13. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh & Shaker, Hamid Reza, 2021. "High dimensional very short-term solar power forecasting based on a data-driven heuristic method," Energy, Elsevier, vol. 219(C).
    14. Gandhi, Oktoviano & Rodríguez-Gallegos, Carlos D. & Zhang, Wenjie & Reindl, Thomas & Srinivasan, Dipti, 2022. "Levelised cost of PV integration for distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    15. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
    16. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    17. Pierro, Marco & De Felice, Matteo & Maggioni, Enrico & Moser, David & Perotto, Alessandro & Spada, Francesco & Cornaro, Cristina, 2020. "Residual load probabilistic forecast for reserve assessment: A real case study," Renewable Energy, Elsevier, vol. 149(C), pages 508-522.
    18. Karim Anaya & Michael Pollitt, 2021. "An evaluation of a local reactive power market: the case of Power Potential," Working Papers EPRG2124, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    19. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    20. Keeratimahat, Kanyawee & Bruce, Anna & MacGill, Iain, 2021. "Analysis of short-term operational forecast deviations and controllability of utility-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 167(C), pages 343-358.

    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:8:p:3518-:d:1126568. 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.