Review of Models for Estimating and Predicting the Amount of Energy Produced by Solar Energy Systems
Abstract and keywords
Abstract (English):
Energy production based on renewable sources is a fundamental aspect of society’s sustainable development. The involvement of renewable energy sources in the implementation of modern energy systems can significantly reduce the amount of harmful emissions into the atmosphere and provide greater flexibility of energy infrastructure. The first step in determining the feasibility of involving a particular energy source in the overall energy system of the region is a preliminary assessment of the energy potential to determine the possible percentage of substitution of traditional energy. To solve this problem, it is necessary to use the models of energy supply, which are currently presented in a wide variety. In this regard, this paper proposes to consider various models for estimating the solar energy potential, which can be divided into empirical models and models based on the application of modern intelligent data analysis technologies. Such models are based on many different climatic and geographical indicators, such as: longitude of sunshine, ambient temperature, serial number of the day of the current year, amount of precipitation, average and maximum values of wind speed and so on. The paper analyzed the existing models for estimating the amount of energy, which can be used in the system designed to determine the most optimal configuration of the energy system based on the use of various conversion technologies most relevant to the case under study, and also serve as the basis for creating digital twins designed to model and optimize the operation of the projected energy complex

Keywords:
potential assessment models, smart models, renewable energy, solar energy
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References

1. Achituev, S. A., and N. Enebish (2015), Evaluation of solar energy potential and photovoltaic (pv) module performance in the regions Buryatia, BSU bulletin. Mathematics, Informatics, 3, 8-15 (in Russian).

2. Agbo, G. A., G. F. Ibeh, and J. E. Ekpe (2012), Estimation of global solar radiation at Onitsha with regression analysis and artificial neural network models, Research Journal of Recent Sciences, 1(6), 27-31.

3. Akdağ, S. A., and A. Dinler (2009), A new method to estimate Weibull parameters for wind energy applications, Energy Conversion and Management, 50(7), 1761-1766, https://doi.org/10.1016/j.enconman.2009.03.020.

4. Akdağ, S. A., and O. Güler (2015), A novel energy pattern factor method for wind speed distribution parameter estimation, Energy Conversion and Management, 106, 1124-1133, https://doi.org/10.1016/j.enconman.2015.10.042.

5. Alsharif, M., M. Younes, and J. Kim (2019), Time series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul, South Korea, Symmetry, 11(2), 240, https://doi.org/10.3390/sym11020240.

6. An, J., D. Yan, S. Guo, Y. Gao, J. Peng, and T. Hong (2020), An improved method for direct incident solar radiation calculation from hourly solar insolation data in building energy simulation, Energy and Buildings, 227, 110,425, https://doi.org/10.1016/j.enbuild.2020.110425.

7. Badescu, V. (2002), 3D isotropic approximation for solar diffuse irradiance on tilted surfaces, Renewable Energy, 26(2), 221-233, https://doi.org/10.1016/S0960-1481(01)00123-9.

8. Belmahdi, B., M. Louzazni, and A. E. Bouardi (2020), One month-ahead forecasting of mean daily global solar radiation using time series models, Optik, 219, 165,207, https://doi.org/10.1016/j.ijleo.2020.165207.

9. Besharat, F., A. A. Dehghan, and A. R. Faghih (2013), Empirical models for estimating global solar radiation: A review and case study, Renewable and Sustainable Energy Reviews, 21, 798-821, https://doi.org/10.1016/j.rser.2012.12.043.

10. Bird, R. E., and R. L. Hulstrom (1981), A simplified clear sky model for direct and diffuse insolation on horizontal surfaces, 39 pp., Solar Energy Research Institute, Colorado.

11. Bugler, J. W. (1977), The determination of hourly insolation on an inclined plane using a diffuse irradiance model based on hourly measured global horizontal insolation, Solar Energy, 19(5), 477-491, https://doi.org/10.1016/0038-092X(77)90103-7.

12. Bulut, H., and O. Büyükalaca (2007), Simple model for the generation of daily global solar-radiation data in Turkey, Applied Energy, 84(5), 477-491, https://doi.org/10.1016/j.apenergy.2006.10.003.

13. Capderou, M. (1985), Atlas solaire de l’algerie: Aspect énérgitique, 399 pp., Office des Publications Universitaires Alger.

14. Cheng, H.-Y., C.-C. Yu, K.-C. Hsu, C.-C. Chan, M.-H. Tseng, and C.-L. Lin (2019), Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles, Energies, 12(8), 1427, https://doi.org/10.3390/en12081427.

15. Cooper, P. I. (1969), The absorption of radiation in solar stills, Solar Energy, 12(3), 333-346, https://doi.org/10.1016/0038-092x(69)90047-4.

16. Demain, C., M. Journée, and C. Bertrand (2013), Evaluation of different models to estimate the global solar radiation on inclined surfaces, Renewable Energy, 50, 710-721, https://doi.org/10.1016/j.renene.2012.07.031.

17. Energy strategy of the Russian Federation for the period until 2035 (2020), Approved by order of the Government of the Russian Federation of June 9, 2020 N 1715-r, https://minenergo.gov.ru/node/1026 (in Russian).

18. EPA (2018), Quantifying the Multiple Benefits of Energy Efficiency and Renewable Energy: A Guide for State and Local Governments, US Environmental Protection Agency, Washington DC, USA.

19. George, F. (2014), A Comparison of Shape and Scale Estimators of the Two-Parameter Weibull Distribution, Journal of Modern Applied Statistical Methods, 13(1), 23-35, https://doi.org/10.22237/jmasm/1398916920.

20. Goliatt, L., and Z. M. Yaseen (2023), Development of a hybrid computational intelligent model for daily global solar radiation prediction, Expert Systems with Applications, 212, 118,295, https://doi.org/10.1016/j.eswa.2022.118295.

21. Granacher, J., T.-V. Nguyen, R. Castro-Amoedo, and F. Maréchal (2022), Overcoming decision paralysis - A digital twin for decision making in energy system design, Applied Energy, 306, 117,954, https://doi.org/10.1016/j.apenergy.2021.117954.

22. Guermoui, M., R. Abdelaziz, K. Gairaa, L. Djemoui, and S. Benkaciali (2020), New temperature-based predicting model for global solar radiation using support vector regression, International Journal of Ambient Energy, 43(1), 1397-1407, https://doi.org/10.1080/01430750.2019.1708792.

23. Guerra, D. D. (2020), Estimation by statistical methods of electric energy generation by electric technical complex with photoelectric panels, News of the Tula state university. Technical sciences, 12, 369-378 (in Russian).

24. Hamilton, H. L., and A. Jackson (1985), A shield for obtaining diffuse sky radiation from portions of the sky, Solar Energy, 34(1), 121-123, https://doi.org/10.1016/0038-092X(85)90099-4.

25. Ibrahim, S., I. Daut, Y. M. Irwan, M. Irwanto, N. Gomesh, and Z. Farhana (2012), Linear Regression Model in Estimating Solar Radiation in Perlis, Energy Procedia, 18, 1402-1412, https://doi.org/10.1016/j.egypro.2012.05.156.

26. IEA (2023), Monthly Electricity Statistics, https://www.iea.org/data-and-statistics/data-tools/monthly-electricity-statistics, (date of access: 14.05.2023).

27. Iktisanov, V., and F. Shkrudnev (2021), Decarbonization: outside view, Energy policy, (8), 42-51, https://doi.org/10.46920/2409-5516_2021_8162_42.

28. IRENA, IEA and REN21 (2018), Renewable Energy Policies in a Time of Transition, Tech. rep., IRENA, OECD/IEA and REN21.

29. Justus, C. G., W. R. Hargraves, A. Mikhail, and D. Graber (1978), Methods for Estimating Wind Speed Frequency Distributions, Journal of Applied Meteorology, 17(3), 350-353, https://doi.org/10.1175/1520-0450(1978)0172.0.CO;2.

30. Klucher, T. M. (1979), Evaluation of models to predict insolation on tilted surfaces, Solar Energy, 23(2), 111-114, https://doi.org/10.1016/0038-092X(79)90110-5.

31. Koholé, Y. W., R. H. Tonsie Djiela, F. C. V. Fohagui, and T. Ghislain (2023), Comparative study of thirteen numerical methods for evaluating Weibull parameters for solar energy generation at ten selected locations in Cameroon, Cleaner Energy Systems, 4, 100,047, https://doi.org/10.1016/j.cles.2022.100047

32. Koronakis, P. S. (1986), On the choice of the angle of tilt for south facing solar collectors in the Athens basin area, Solar Energy, 36(3), 217-225, https://doi.org/10.1016/0038-092X(86)90137-4.

33. Kwon, Y., A. Kwasinski, and A. Kwasinski (2019), Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables, Energies, 12(8), 1529, https://doi.org/10.3390/en12081529.

34. Li, H., W. Ma, X. Wang, and Y. Lian (2011), Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study, Renewable Energy, 36(7), 1944-1948, https://doi.org/10.1016/j.renene.2011.01.006.

35. Liu, B. Y. H., and R. C. Jordan (1962), Daily insolation on surfaces tilted towards the equator, Ashrae Transactions, 67, 526-541.

36. Liu, Z., Z. Deng, G. He, H. Wang, X. Zhang, J. Lin, Y. Qi, and X. Liang (2021), Challenges and opportunities for carbon neutrality in China, Nature Reviews Earth & Environment, 3(2), 141-155, https://doi.org/10.1038/s43017-021-00244-x.

37. Lysen, E. H. (1982), Introduction to wind energy, 309 pp., Consultancy services wind energy developing countries, Netherlands.

38. Ma, C. C. Y., and M. Iqbal (1983), Statistical comparison of models for estimating solar radiation on inclined surgaces, Solar Energy, 31(3), 313-317, https://doi.org/10.1016/0038-092x(83)90019-1.

39. Mghouchi, Y. E., A. E. Bouardi, Z. Choulli, and T. Ajzoul (2016), Models for obtaining the daily direct, diffuse and global solar radiations, Renewable and Sustainable Energy Reviews, 56, 87-99, https://doi.org/10.1016/j.rser.2015.11.044.

40. Moldovan, C. L., R. Păltănea, and I. Visa (2020), Improvement of clear sky models for direct solar irradiance considering turbidity factor variable during the day, Renewable Energy, 161, 559-569, https://doi.org/10.1016/j.renene.2020.07.086.

41. Muneer, T., and H. Kambezidis (1997), Solar radiation and daylight models for the energy efficient design of buildings, Architectural press, Boston.

42. Narasimman, K., V. Gopalan, A. K. Bakthavatsalam, P. V. Elumalai, M. I. Shajahan, and J. J. Michael (2023), Modelling and real time performance evaluation of a 5 MW grid-connected solar photovoltaic plant using different artificial neural networks, Energy Conversion and Management, 279, 116,767, https://doi.org/10.1016/j.enconman.2023.116767.

43. Onishchenko, S. V., K. A. Kuzmin, and T. Y. Bychkov (2022), Development of a digital twin concept for an autonomous complex for assessing the energy potential of renewable energy sources, in Proceedings of the International Scientific and Practical Conference Applied Issues of Exact Sciences, Armavir, Armenia, 16-17 October 2022, pp. 178-180 (in Russian).

44. Pang, Z., F. Niu, and Z. O’Neill (2020), Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons, Renewable Energy, 156, 279-289, https://doi.org/10.1016/j.renene.2020.04.042.

45. Perez, R., R. Seals, P. Ineichen, R. Stewart, and D. Menicucci (1987), A new simplified version of the perez diffuse irradiance model for tilted surfaces, Solar Energy, 39(3), 221-231, https://doi.org/10.1016/S0038-092X(87)80031-2.

46. Perrin de Brichambaut, C. (1975), Estimation Des Ressources Energetiques Solaires en France. Supplement aux cahiers AFEDES, 1, Association francaise pour l’etude et le developpement des applications de l’energie solaire.

47. Polasek, T., and M. Čadík (2023), Predicting photovoltaic power production using high-uncertainty weather forecasts, Applied Energy, 339, 120,989, https://doi.org/10.1016/j.apenergy.2023.120989.

48. REN21 (2020), Renewables 2020 Global Status Report, Tech. rep., REN21 Secretariat.

49. REN21 (2021), Renewables 2021 Global Status Report, Tech. rep., REN21 Secretariat, Paris.

50. Rigollier, C., M. Lefèvre, S. Cros, and L. Wald (2002), Heliosat 2: an improved method for the mapping of the solarradiation from Meteosat imagery, in Proceedings of the 2002 EUMETSAT Meteorological SatelliteConference, Dublin, Ireland, 1-6 September 2002, pp. 585-592, EUMETSAT.

51. Robaa, S. M. (2009), Validation of the existing models for estimating global solar radiation over Egypt, Energy Conversion and Management, 50(1), 184-193, https://doi.org/10.1016/j.enconman.2008.07.005.

52. Rusen, S. E., A. Hammer, and B. G. Akinoglu (2013), Coupling satellite images with surface measurements of bright sunshine hours to estimate daily solar irradiation on horizontal surface, Renewable Energy, 55, 212-219, https://doi.org/10.1016/j.renene.2012.12.019.

53. Russia Renewable Energy Development Association (RREDA) (2022), Russian Renewable Energy Market Review, Q3 2022, https://rreda.ru/en/reports/quarter-reports/1345/ (in Russian), (date of access: 01.05.2023).

54. Saïghi, M. (2002), Nouveau modèle de transfert hydrique dans le système sol - plante - atmosphére continuum.

55. Simankov, V. S. (2002), Automation of systems research: a monograph, 376 pp., KubSTU, Krasnodar (in Russian).

56. Simankov, V. S., and P. Y. Buchatskiy (2019), Complex of mathematical models of the renewable energy for a forward-looking assesment of its potential, in III International Scientific Conference "Autumn Mathematical Readings in Adygeya", Maykop, 15-20 October 2019, vol. III, pp. 122-124 (in Russian).

57. Simankov, V. S., and P. Y. Buchatskiy (2021), Methodological foundations of innovative solutions in renewable energy engineering, The Bulletin of the Adyghe State University, the series "Natural-Mathematical and Technical Sciences", (3(286)), 42-54, https://doi.org/10.53598/2410-3225-2021-3-286-42-54 (in Russian).

58. Simankov, V. S., P. Y. Buchatskiy, and A. V. Shopin (2000), Modelling insolation with control photowindenergy systems, Works of the Adygheya Republic Physical Society, (5), 67-71 (in Russian).

59. Simankov, V. S., A. N. Cherkasov, V. V. Buchatskaya, and S. V. Teploukhov (2021a), Situational center as an intelligent decision support system taking into account the uncertainty of the source information, in CEUR Workshop Proceedings. 4th All-Russian Scientific and Practical Conference with International Participation "Distance Learning Technologies", DLT 2019 Yalta, Crimea, 16-21 September 2019, pp. 404-414.

60. Simankov, V. S., I. G. Gorin, and A. V. Tsekhomskiy (2021b), Integration of simulation systems in a situation center, in Modern scientific hypotheses and forecasts: from theory to practice: a collection of scientific articles based on the results of the international scientific and practical conference. August30-31, 2021. Saint-Petersburg, pp. 20-23, Publishing House of SPbSUE, St. Petersburg (in Russian).

61. Simankov, V. S., P. Y. Buchatskiy, S. V. Teploukhov, and V. V. Buchatskaya (2022), Knowledge Management Subsystem of the Intellectual Situational Center, in 2022 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), IEEE, https://doi.org/10.1109/itqmis56172.2022.9976535.

62. Souhaila, C., and M. Mohamed (2021), Ensemble methods comparison to predict the Power produced by Photovoltaic Panels, Procedia Computer Science, 191, 385-390, https://doi.org/10.1016/j.procs.2021.07.049.

63. Stevens, M. J. M., and P. T. Smulders (1979), The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes, Wind Engineering, 3(2), 132-145.

64. Takilalte, A., S. Harrouni, and J. Mora (2019), Forecasting global solar irradiance for various resolutions using time series models - case study: Algeria, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(1), 1-20, https://doi.org/10.1080/15567036.2019.1649756.

65. Teke, A., H. B. Yıldırım, and O. Çelik (2015), Evaluation and performance comparison of different models for the estimation of solar radiation, Renewable and Sustainable Energy Reviews, 50, 1097-1107, https://doi.org/10.1016/j.rser.2015.05.049.

66. Tonsie Djiela, R. H., P. T. Kapen, and G. Tchuen (2020), Wind energy of Cameroon by determining Weibull parameters: potential of a environmentally friendly energy, International Journal of Environmental Science and Technology, 18(8), 2251-2270, https://doi.org/10.1007/s13762-020-02962-z.

67. Tyunkov, D. A., A. S. Gritsay, V. I. Potapov, R. N. Khamitov, A. V. Blohin, and L. K. Kondratukova (2019), Short-term forecast methods of electricity generation by solar power plants and its classification, Journal of Physics: Conference Series, 1260(5), 052,033, https://doi.org/10.1088/1742-6596/1260/5/052033.

68. Vaskov, A. G., and A. F. Narynbaev (2020), Solar Radiation Estimation and Prediction Methods: a Review and Classification, Vestnik MEI, 4(4), 49-61, https://doi.org/10.24160/1993-6982-2020-4-49-61 (in Russian).

69. Willmott, C. J. (1982), On the climatic optimization of the tilt and azimuth of flat-plate solar collectors, Solar Energy, 28(3), 205-216, https://doi.org/10.1016/0038-092x(82)90159-1.

70. Wu, Y.-K., C.-R. Chen, and H. A. Rahman (2014), A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation, International Journal of Photoenergy, 2014, 1-9, https://doi.org/10.1155/2014/569249.

71. Yin, K., X. Zhang, J. Xie, Z. Hao, G. Xiao, and J. Liu (2023), Modeling hourly solar diffuse fraction on a horizontal surface based on sky conditions clustering, Energy, 272, 127,008, https://doi.org/10.1016/j.energy.2023.127008.

72. Yuzer, E. O., and A. Bozkurt (2023), Deep learning model for regional solar radiation estimation using satellite images, Ain Shams Engineering Journal, 14(8), 102,057, https://doi.org/10.1016/j.asej.2022.102057.

73. Zang, H., X. Jiang, L. Cheng, F. Zhang, Z. Wei, and G. Sun (2022), Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations, Renewable Energy, 195, 795-808, https://doi.org/10.1016/j.renene.2022.06.063.

74. Zhao, B., X. Zhang, J. Chen, C. Wang, and L. Guo (2013), Operation Optimization of Standalone Microgrids Considering Lifetime Characteristics of Battery Energy Storage System, IEEE Transactions on Sustainable Energy, 4(4), 934-943, https://doi.org/10.1109/tste.2013.2248400.

75. Zhu, D., T. Hong, D. Yan, and C. Wang (2012), Comparison of Building Energy Modeling Programs: Building Loads, Tech. rep., Ernest Orlando Lawrence Berkeley National Laboratory

76. Zohbi, A. G., P. Hendrick, and P. Bouillard (2014), Evaluation du potentiel d’énergie éolienne au Liban, Revue des Energies Renouvelables, 17(1), 83-96.

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