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Accuracy Analysis of Estimates of Total Solar Radiation in Databases and Regression Models for Eastern Russia

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Abstract

The efficient use of solar energy requires an accurate assessment of the incoming solar radiation. The study involves comparing the accuracy of monthly means of the global solar radiation flux from the ERA5 reanalysis database, the SYN1deg satellite-based observation database, climate reference data, and data of regression models for seven settlements in the east of Russia. The study employs two well-known regression models, including parameters of extraterrestrial solar radiation, total cloudiness, air humidity, minimum and maximum air temperature, atmospheric pressure, and a new regression model, which additionally includes parameters of low-level cloudiness and sun elevation angle. The accuracy of databases and regression models is evaluated by comparing their data with ground measurements of weather stations. The indices of the mean absolute error, root-mean-square error, and mean bias error are calculated. The comparison shows that the data of climate reference books for the period from 1937–1957 to 1980 have the smallest deviation from the estimates of the monthly mean flux of global solar radiation for 2006–2020 at most of the points discussed. The ERA5 monthly mean estimates of the global solar radiation flux are more accurate than the SYN1deg data at five of the seven points considered. The new regression model proposed in the study makes it possible to provide greater accuracy of monthly estimates of the global solar radiation flux compared to the data of SYN1deg and ERA5 for most of the points considered.

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REFERENCES

  1. Anam, Md.Z., Bari, A.B.M.M., Paul, S.K., Ali, S.M., and Kabir, G., Modelling the drivers of solar energy development in an emerging economy: Implications for sustainable development goals, Resour., Conserv. Recycling Adv., 2022, vol. 13, p. 200068. https://doi.org/10.1016/j.rcradv.2022.200068

    Article  Google Scholar 

  2. Li, J. and Huang, J., The expansion of China’s solar energy: Challenges and policy options, Renewable Sustainable Energy Rev., 2020, vol. 132, p. 110002. https://doi.org/10.1016/j.rser.2020.110002

    Article  Google Scholar 

  3. Hoang, A.T., Pham, V.V., and Nguyen, X.P., Integrating renewable sources into energy system for smart city as a sagacious strategy towards clean and sustainable process, J. Cleaner Production, 2021, vol. 305, p. 127161. https://doi.org/10.1016/j.jclepro.2021.127161

    Article  Google Scholar 

  4. Miravet-Sánchez, B.L., García-Rivero, A.E., Yuli-Posadas, R.A., Inostroza-Ruiz, L.A., Fernández-Guzmán, V., Chávez-Juanito, Y.A., Rutti-Marinf, J.M., and Apesteguia-Infantes, J.A., Solar photovoltaic technology in isolated rural communities in Latin America and the Caribbean, Energy Rep., 2022, vol. 8, no. 6, pp. 1238–1248.

    Article  Google Scholar 

  5. Rahman, M.M., Khan, I., Field, D.L., Techato, K., and Alameh, K., Powering agriculture: Present status, future potential, and challenges of renewable energy applications, Renewable Energy, 2022, vol. 188, pp. 731–749.

    Article  Google Scholar 

  6. Renné, D.S., Progress, opportunities and challenges of achieving net-zero emissions and 100% renewable, Solar Compass, 2022, vol. 1, Article 100007. https://doi.org/10.1016/j.solcom.2022

    Article  Google Scholar 

  7. Neishtadt, Ya.A. and Chervyakov, M.Yu Assessment of the climatic potential for the development of solar energy in the southeast of the European territory of Russia according to satellite and ground-based observations, Trudy Mezhdunar. Baikal. molodezh. nauch. shkoly po fundamental’noi fizike i XVI Konferentsii molodykh uchenykh (Proc. Int. Baikal. Youth Sci. School of Fundamental Physics and the XVI Conference of Young Scientists), Irkutsk: Inst. Solnechno-Zemnoi Fiz. Sib. Otdel. Ross. Akad. Nauk, Irkutsk, 2019, pp. 354–356.

  8. Luts’ko, L.V. and Makhotkina, E.L., On the modernization of the meteorological network, Tr. Glavn. Geofiz. Observatorii im. A.I. Voeikova, 2011, no. 564, pp. 51–65.

  9. Luts’ko, L.V., Erokhina, A.E., Bychkova, A.P., Makhotkina, E.L., Makhotkin, A.N., and Bekeneva, O.B., Actinometric network of Roshydromet: Current state, Tr. Glavn. Geofiz. Observatorii im. A.I. Voeikova, 2018, no. 589, pp. 125–152.

  10. Nematchoua, M.K., Orosa, J.A., and Afaifia, M., Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries, Ecol. Informatics, 2022, vol. 69, Article 101643. https://doi.org/10.1016/j.ecoinf.2022.101643

    Article  Google Scholar 

  11. Cao, Q., Liu, Y., Sun, X., and Yang, L., Country-level evaluation of solar radiation data sets using ground measurements in China, Energy, 2022, vol. 241, Article 122938. https://doi.org/10.1016/j.energy.2021.122938

    Article  Google Scholar 

  12. Yang, D. and Bright, J.M., Worldwide validation of 8 satellite-derived and reanalysis solar radiation products: A preliminary evaluation and overall metrics for hourly data over 27 years, Solar Energy, 2020, vol. 210, pp. 3–19.

    Article  Google Scholar 

  13. Mirzabe, A.H., Hajiahmad, A., Keyhani, A., and Mirzaei, N., Approximation of daily solar radiation: A comprehensive review on employing of regression models, Renewable Energy Focus, 2022, vol. 41, pp. 143–59.

    Article  Google Scholar 

  14. Chen, J.L., He, L., Yang, H., Ma, M., Chen, Q., Wu, S.J., and Xiao, Z., Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China, Renewable Sustainable Energy Rev., 2019, vol. 108, pp. 91–111.

    Article  Google Scholar 

  15. Babar, B., Graversen, R., and Boström, T., Solar radiation estimation at high latitudes: Assessment of the CMSAF databases, ASR and ERA5, Solar Energy, 2019, vol. 82, pp. 397–411.

    Article  Google Scholar 

  16. Huang, G., Li, Z., Li, X., Liang, S., Yang, K., Wang, D., and Zhang, Y., Estimating surface solar irradiance from satellites: Past, present, and future perspectives, Remote Sens. Envir., 2019, vol. 233, Article 111371. https://doi.org/10.1016/j.rse.2019.111371

    Article  Google Scholar 

  17. European Centre for Medium-Range Weather Forecasts. https://www.ecmwf.int/en/forecasts/datasets/ reanalysis-datasets/era5. Cited July 12, 2022.

  18. CERES Data Products. https://ceres.larc.nasa.gov/ data/. Cited July 12, 2022.

  19. Nauchno-prikladnoi spravochnik po klimatu SSSR, Ser. 3: Mnogoletnie nablyudeniya, Vyp. 24: Yakutskaya ASSR (Scientific and Applied Reference Book on the Climate of the USSR, ser. 3: Long-Term Observation, no. 24: Yakut Autonomous Soviet Socialist Republic), Kobysheva, N.V, Ed., Leningrad: Gidrometeoizdat, 1989.

  20. Khlebnikova, E.I., Makhotkina, E.L., and Sall’, I.A., Cloudiness and radiation regime on the territory of Russia: Observed climate changes, Tr. Glavn. Geofiz. Observatorii im. A.I. Voeikova, 2014, no. 573, pp. 65–91.

  21. World Radiation Data Center. http://wrdc.-mgo.rssi.ru/. Cited July 12, 2022.

  22. Soulouknga, M.H., Coulibaly, O., Doka, S.Y., and Kofane, T.C., Evaluation of global solar radiation from meteorological data in the Sahelian zone of Chad, Renewables: Wind, Water, and Solar, 2017, vol. 4, 4, Article 4. https://doi.org/10.1186/s40807-017-0041-0

    Article  Google Scholar 

  23. Weather schedule. https://rp5.ru/. Cited July 12, 2022.

  24. Shakirov, V.A., Yakovkina, T.N., and Kurbatskii, V.G., Methodology for assessing electricity production by solar power plants using data from long-term observations of weather stations, Vestn. Irkutsk. Gos. Tekh. Univ., 2020, vol. 24, no. 4 (153), pp. 858–875.

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Funding

The study was performed as part of research under the draft state assignment (FWEU-2021-0004), part of the program of fundamental research in the Russian Federation for 2021–2030, using resources of the Collective Use Center “High-Temperature Circuit” (Ministry of Science and Higher Education of the Russian Federation, project 13. TsKP.21.0038).

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Authors and Affiliations

  1. Melentiev Energy Systems Institute, Russian Academy of Sciences, Siberian Branch, 664033, Irkutsk, Russia

    I. Yu. Ivanova, V. A. Shakirov & N. A. Khalgaeva

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  1. I. Yu. Ivanova
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  2. V. A. Shakirov
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  3. N. A. Khalgaeva
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Correspondence to I. Yu. Ivanova, V. A. Shakirov or N. A. Khalgaeva.

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Translated by A. Ovchinnikova

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Ivanova, I.Y., Shakirov, V.A. & Khalgaeva, N.A. Accuracy Analysis of Estimates of Total Solar Radiation in Databases and Regression Models for Eastern Russia. Geogr. Nat. Resour. 44, 278–283 (2023). https://doi.org/10.1134/S1875372823030058

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