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Evaluation of High Resolution WRF Solar

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  • 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
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    References listed on IDEAS

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    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.
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    1. Fadhilah A. Suwadana & Pranda M. P. Garniwa & Dhavani A. Putera & Dita Puspita & Ahmad Gufron & Indra A. Aditya & Hyunjin Lee & Iwa Garniwa, 2025. "Solar Irradiance Estimation in Tropical Regions Using Recurrent Neural Networks and WRF Models," Energies, MDPI, vol. 18(4), pages 1-17, February.
    2. Amorim, Ana Cleide Bezerra & de Almeida Dantas, Vanessa & dos Reis, Jean Souza & de Assis Bose, Nicolas & Emiliavaca, Samira de Azevedo Santos & Cruz Bezerra, Luciano André & de Matos, Maria de Fátima, 2024. "Analysis of WRF-solar in the estimation of global horizontal irradiation in Amapá, northern Brazil," Renewable Energy, Elsevier, vol. 235(C).

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