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A Novel Approach to Enhance the Generalization Capability of the Hourly Solar Diffuse Horizontal Irradiance Models on Diverse Climates

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  • Raghuram Kalyanam

    (Chair of Built Environment, Faculty of Civil Engineering, University of Kaiserslautern, Paul-Ehrlich-Str. 14, 67663 Kaiserslautern, Germany)

  • Sabine Hoffmann

    (Chair of Built Environment, Faculty of Civil Engineering, University of Kaiserslautern, Paul-Ehrlich-Str. 14, 67663 Kaiserslautern, Germany)

Abstract

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m 2 , but also generalized very well on European climates with MAE in the range of 20 to 27 W/m 2 . Further investigation into the factors influencing the generalizability revealed that careful selection of the training data can improve the results significantly.

Suggested Citation

  • Raghuram Kalyanam & Sabine Hoffmann, 2020. "A Novel Approach to Enhance the Generalization Capability of the Hourly Solar Diffuse Horizontal Irradiance Models on Diverse Climates," Energies, MDPI, vol. 13(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4868-:d:415092
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    References listed on IDEAS

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    1. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    2. Beckman, William A. & Broman, Lars & Fiksel, Alex & Klein, Sanford A. & Lindberg, Eva & Schuler, Mattias & Thornton, Jeff, 1994. "TRNSYS The most complete solar energy system modeling and simulation software," Renewable Energy, Elsevier, vol. 5(1), pages 486-488.
    3. Elminir, Hamdy K. & Azzam, Yosry A. & Younes, Farag I., 2007. "Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models," Energy, Elsevier, vol. 32(8), pages 1513-1523.
    4. Saioa Etxebarria Berrizbeitia & Eulalia Jadraque Gago & Tariq Muneer, 2020. "Empirical Models for the Estimation of Solar Sky-Diffuse Radiation. A Review and Experimental Analysis," Energies, MDPI, vol. 13(3), pages 1-23, February.
    5. Soares, Jacyra & Oliveira, Amauri P. & Boznar, Marija Zlata & Mlakar, Primoz & Escobedo, João F. & Machado, Antonio J., 2004. "Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique," Applied Energy, Elsevier, vol. 79(2), pages 201-214, October.
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