Comparative Study of Horizontal Surface Solar Radiation for Different South Asian Zones
Document Type : Research Article
Authors
Department of Mathematics, Chittagong University of Engineering & Technology, Chattogram, Bangladesh
Abstract
This study presents a comparative analysis of solar radiation across selected South Asian regions using the Angstrom–Prescott linear regression model. This model estimates monthly average global solar radiation on horizontal surfaces from bright sunshine duration, considering latitude and longitude. The monthly average solar radiation data for 2025 were calculated and compared with model-based estimates across different zones, demonstrating strong agreement between observed and predicted values through graphical and statistical analyses. Among the eight locations we analyzed, Male received the highest annual solar radiation value 3890.61, whereas Kabul recorded the lowest 3146.65 .The seasonal variations indicated that Male experienced higher solar radiation and brighter sunshine duration from February to June and July to September, with values ranging from (36.12 to 40.23 ).In contrast, Kabul’s sunshine duration during the same periods was lower, ranging between (28.91 to 33.25 ) . Both locations showed a decline in sunshine duration between October and January, with Male ranging from (40.31 to 38.96 ) and Kabul from (32.91 to 30.68 ).Additionally, clearness index analysis confirmed that Male experienced the clearest atmospheric conditions with minimal cloud cover, whereas Kabul was more affected by cloudiness. These results provide valuable insights for future solar energy assessments in South Asia.
Keywords
[1] Nadim, M., Rashed, M. R. H., Muhury, A., & Mominuzzaman, S. M. (2016). Estimation of optimum tilt angle for PV cell: A study in perspective of Bangladesh. Paper presented at the 2016 9th International Conference on Electrical and Computer Engineering (ICECE).DOI:10.1109/ICECE.2016.7853908
[2] Islam, M. A., Alam, M. S., Sharker, K. K., & Nandi, S. K. (2016). Estimation of solar radiation on horizontal and tilted surface over Bangladesh. Computational Water, Energy, and Environmental Engineering, 5(2), 54-69. DOI:10.4236/cweee.2016.52006
[3] Besharat, F., Dehghan, A. A., & Faghih, A. R. (2013). Empirical models for estimating global solar radiation: A review and case study. Renewable and Sustainable Energy Reviews, 21, 798-821. DOI:10.1016/j.rser.2012.12.043
[4] Pandey, B., Aryal, R., Gnawali, C., Poudyal, K., Karki, I., & Koirala, I. (2019). Estimation of Monthly average daily diffuse solar radiation using empirical models for Kathmandu Nepal. Journal of Nepal Physical Society, 5(1), 6-13. DOI:10.3126/jnphyssoc.v5i1.26875
[5] Ilboudo, J.M., Bonkoungou, D., Tassembedo, S., and Koalaga, Z. (2024). General models for monthly average daily global solar irradiation. Science Journal of Energy Engineering, 12(4), 81–90. DOI:10.11648/j.sjee.20241204.12
[6] Makade, R. G., Chakrabarti, S., & Jamil, B. (2019). Prediction of global solar radiation using a single empirical model for diversified locations across India. Urban Climate, 29, 100492. DOI:10.1016/j.uclim.2019.100492
[7] Mohammadi, B., & Moazenzadeh, R. (2021). Performance analysis of daily global solar radiation models in Peru by regression analysis. Atmosphere, 12(3), 389. DOI:10.3390/atmos12030389
[8] Kalogirou, S. A. (2014). Designing and modeling solar energy systems. Solar energy engineering, 583-699. DOI:10.1016/B978-0-12-397270-5.00011-X
[9] Khanlari, A., Sözen, A., Şirin, C., Tuncer, A. D., & Gungor, A. (2020). Performance enhancement of a greenhouse dryer: Analysis of a cost-effective alternative solar air heater. Journal of Cleaner Production, 251, 119672. DOI:10.1016/j.jclepro.2019.119672
[10] Ben Othman, A., Belkilani, K., & Besbes, M. (2020). Prediction improvement of potential PV production pattern, imagery satellite-based. Scientific Reports, 10(1), 19951. DOI:10.1038/s41598-020-76957-8
[11] Tamim, A. (2021). Assessment of solar energy potential and development in Afghanistan. Paper presented at the Proc. E3S Web Conf.DOI:10.1051/e3sconf/202123900012
[12] Ahmad, N., Ghadi, Y. G., Adnan, M., & Ali, M. (2023). From smart grids to super smart grids: a roadmap for strategic demand management for next generation SAARC and European power infrastructure. IEEE Access, 11, 12303-12341. DOI:10.1109/ACCESS.2023.3241686
[13] Jahangiri, M., Haghani, A., Mostafaeipour, A., Khosravi, A., & Raeisi, H. A. (2019). Assessment of solar-wind power plants in Afghanistan: A review. Renewable and Sustainable Energy Reviews, 99, 169-190.DOI:10.1016/j.rser.2018.10.003
[14] Malik, P., Gehlot, A., Singh, R., Gupta, L. R., & Thakur, A. K. (2022). A review on ANN based model for solar radiation and wind speed prediction with real-time data. Archives of Computational Methods in Engineering, 29(5), 3183-3201.DOI:10.1007/s11831-021-09687-3
[15] Liaqat, M., Ghadi, Y., & Adnan, M. (2021). Multi-objective optimal power sharing model for futuristic SAARC super smart grids. IEEE Access, 10, 328-351. DOI:10.1109/ACCESS.2021.3137592
[16] Bangladesh Energy Reports. (2022). AnnualPower Sector Report
[17] Ordoñez Palacios, L. E., Bucheli Guerrero, V., & Ordoñez, H. (2022). Machine learning for solar resource assessment using satellite images. Energies, 15(11), 3985. DOI:10.3390/en15113985
[18] Ul-Haq, A., Jalal, M., Hassan, M. S., Sindi, H., Ahmad, S., & Ahmad, S. (2021). Implementation of smart grid technologies in Pakistan under CPEC project: technical and policy implications. IEEE Access, 9, 61594-61610.DOI:10.1109/ACCESS.2021.3074338
[19]Chodakowska, E., Nazarko, J., Nazarko, Ł., Rabayah, H. S., Abendeh, R. M., & Alawneh, R. (2023). ARIMA models in solar radiation forecasting in different geographic locations. Energies, 16(13), 5029. DOI:10.3390/en16135029
[20] Manzoor, H. U., Aaqib, S. M., Manzoor, T., Azeem, F., Ashraf, M. W., & Manzoor, S. (2025). Effect of Optimized Tilt Angle of PV Modules on Solar Irradiance for Residential and Commercial Buildings in Different Cities of Pakistan: Simulation‐Based Study. Energy Science & Engineering, 13(4), 1831-1845. DOI:10.1002/ese3.70004
[21] Khalid, H. M., Rafique, Z., Muyeen, S., Raqeeb, A., Said, Z., Saidur, R., & Sopian, K. (2023).Dust accumulation and aggregation on PV panels: An integrated survey on impacts, mathematical models, cleaning mechanisms, and possible sustainable solution Solar Energy, 259, 277. DOI:10.1016/j.solener.2023.05.036
[22] Rajagukguk, R. A., & Lee, H. (2023). Enhancing the performance of solar radiation decomposition models using deep learning. Journal of the Korean Solar Energy Society, 43(3), 73-86. DOI:10.7836/kses.2023.43.3.073
[23] Nadeem, T. B., Ali, S. U., Asif, M., & Suberi, H. K. (2024). Forecasting daily solar radiation: An evaluation and comparison of machine learning algorithms. AIP Advances, 14(7). DOI:10.1063/5.0211723
[24] Gyeltshen, S., Hayashi, K., Tao, L., & Dem, P. (2025). Statistical evaluation of a diversified surface solar irradiation data repository and forecasting using a recurrent neural network-hybrid model: A case study in Bhutan. Renewable Energy, 245, 122706. DOI: 10.1016/j.renene.2025.122706
[25] RajasundrapandiyanLeebanon, T., Murugan, N., Kumaresan, K., & Jeyabose, A. (2025). Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms. Environmental Monitoring and Assessment, 197(3), 1-36. DOI:10.1007/s10661-025-13738-8
[26] World Bank SE4ALL. (2020). Global Energy Tracking Framework. Washington DC.
[27] Pereira, L. S., Allen, R. G., Smith, M., & Raes, D. (2015). Crop evapotranspiration estimation with FAO56: Past and future. Agricultural water management, 147, 4-20.DOI: 10.1016/j.agwat.2014.07.031
[28] Keshtegar, B., Bouchouicha, K., Bailek, N., Hassan, M. A., Kolahchi, R., & Despotovic, M. (2022). Solar irradiance short-term prediction under meteorological uncertainties: survey hybrid artificial intelligent basis music-inspired optimization models. The European Physical Journal Plus, 137(3), 362. DOI:10.1140/epjp/s13360-022-02371-w
[29] Rajagukguk, R. A., & Lee, H. (2025). Application of explainable machine learning for estimating direct and diffuse components of solar irradiance. Scientific Reports, 15(1), 7402. DOI:10.1038/s41598-025-91158-x
[30] Saud, J. S., Shrestha, P. M., Joshi, U., Tiwari, B. R., Karki, I. B., & Poudyal, K. N. (2023). Estimation of Global Solar Radiation using Angstrom and Gopinathan Model on Sunshine Hour and Temperature in Highland, Nepal. Molung Educational Frontier, 92-107. DOI:10.3126/mef.v13i01.56094
[31] Qi, Q., Wu, J., Gueymard, C. A., Qin, W., Wang, L., Zhou, Z., . . . Zhang, M. (2024). Mapping of 10-km daily diffuse solar radiation across China from reanalysis data and a Machine-Learning method. Scientific Data, 11(1), 756. DOI:10.1038/s41597-024-03609-1
[32] Attya M., Abo-Seida O., Mohamed H., Mohammed A. (2025). A hybrid deep learning framework for solar irradiation prediction based on regional satellite images and data. Neural Computing and Applications. 1-37. DOI:10.1007/s00521-025-11197-3
[33]Santhakumari M., Nalla S., Naick B.P., Mavi S., Chandrapuri S.(2025).Attenuation effect of air pollution on global solar irradiation-Evidence from the Indian cities.DOI:10.2139/ssrn.5587679
[34]Al-Shourbaji I., Alameen A.(2025). Optimizing Solar Radiation Prediction with ANN and Explainable AI-Based Feature Selection. Technologies.13(7),263.DOI:10.3390/technologies13070263
[35] Baran S., Marín JC., Cuevas O., Díaz M., Szabo M., Nicolis O., et al.(2025). Machine-learning-based probabilistic forecasting of solar irradiance in Chile. Advances in Statistical Climatology, Meteorology and Oceanography. 11(1), 89-105.DOI:10.5194/ascmo-11-89-2025
[36] Rashid, M.-A., Mamun, R., Sultana, J., Hasnat, A., Khan, K., & Rahman, M. (2012). Evaluating the Solar Radiation System under the Climatic Condition of Dhaka, Bangladesh and Computing the Angstrom Coefficients. International Journal of Natural Sciences, 2(1), 38-42. DOI:10.3329/ijns.v2i1.10882
[37] Basunia, M., Yoshio, H., & Abe, T. (2012). Simulation of solar radiation incident on horizontal and inclined surfaces. The Journal of Engineering Research, 9(2), 27-35. DOI:10.24200/tjer.vol9iss2pp27-35
[38] Miranda, E., Fierro, J. F. G., Narváez, G., Giraldo, L. F., & Bressan, M. (2021). Prediction of site-specific solar diffuse horizontal irradiance from two input variables in Colombia. Heliyon, 7(12). DOI:10.1016/j.heliyon.2021.e08602
[40] Collares-Pereira, M., & Rabl, A. (1979). The average distribution of solar radiation-correlations between diffuse and hemispherical and between daily and hourly insolation values. Solar Energy, 22(2),155-164.DOI:10.1016/0038-092X(79)90100-2
[41] Cooper, P. (1969). The absorption of radiation in solar stills. Solar Energy, 12(3), 333-346. DOI:10.1016/0038-092X(69)90047-4
[42]Sarkar, M. N. I., & Sifat, A. I. (2016). Global solar radiation estimation from commonly available meteorological data for Bangladesh. Renewables: Wind, Water, and Solar, 3(1), 6. DOI:10.1186/s40807-016-0027-3
[43] Singh, A., Singh, S., Srivastava, P., & Jain, A. (2025). Angstrom-Prescott, Artificial and Convolutional neural network radiation models over North India. Earth Science Informatics, 18(1), 158. DOI:10.1007/s12145-024-01618-7
)