Solar Forecasting Methods: Transition from Classical to Artificial Intelligence-based Forecasting

Abstract

The variability in photovoltaic power generation generates different negative effects on power grid systems in terms of stability, reliability, and operation planning. Therefore, an accurate estimation of PV power generation is crucial for stabilizing and securing grid operation and promoting large-scale PV power integration. Every year, new techniques and approaches emerge worldwide that reduce the uncertainty in these estimates and improve model accuracy. This study presents a comprehensive survey of solar energy prediction models while summarizing the most recent methodologies and strategies employed to enhance the precision of solar energy production forecasting. The energy sector is highly dynamic and integrates new technologies continually; hence, compilation studies should be updated with new developments. Industries are trying to benefit from artificial intelligence, including the field of solar energy prediction, and this study attempts to capture this trend. In addition to presenting a comparison of the solution methods in artificial intelligence, hybrid approaches to overcome problems are discussed. Different classification models and critical analyses of recent studies based on forecast horizons and historical data are also presented.

Keywords

• Classifications
• day-ahead forecast
• forecast horizon
• historical data
• PV power generation
• solar forecasting

References

1. [1] S. R. Weart, The discovery of global warming. Harvard University Press, 2008.
2. [2] S. K. Jha and H. Puppala, “Prospects of renewable energy sources in India: Prioritization of alternative sources in terms of Energy Index,” Energy, vol. 127, pp. 116–127, 2017.
3. [3] S. Kumar, B. Bhattacharyya, and V. K. Gupta, “Present and future energy scenario in India,” Journal of The Institution of Engineers (India): Series B, vol. 95, no. 3, pp. 247–254, 2014.
4. [4] A. Awasthi et al., “Review on sun tracking technology in solar PV system,” Energy Reports, vol. 6, pp. 392–405, 2020.
5. [5] J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres, “Review of photovoltaic power forecasting,” Solar energy, vol. 136, pp. 78–111, 2016.
6. [6] D. Bogdanov et al., “Low-cost renewable electricity as the key driver of the global energy transition towards sustainability,” Energy, vol. 227, p. 120467, 2021.
7. [7] T. André, “Renewables 2020 Global Status Report,” REN21, Paris, France, 2020.
8. [8] L. Yavuz, A. Önen, S. M. Muyeen, and I. Kamwa, “Transformation of microgrid to virtual power plant–a comprehensive review,” IET Generation, Transmission & Distribution, vol. 13, no. 11, pp. 1994–2005, 2019.

9. [9] A. Asrari, T. X. Wu, and B. Ramos, “A hybrid algorithm for short-term solar power prediction—Sunshine state case study,” IEEE Trans Sustain Energy, vol. 8, no. 2, pp. 582–591, 2016.
10. [10] BP, “BP.BP Statistical Review of World Energy. London: BP; 2021,” london, 2021.
11. [11] M. C. Kocer et al., “Cloud induced PV impact on voltage profiles for real microgrids,” in 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA), IEEE, 2018, pp. 1–6.
12. [12] M. Q. Raza, M. Nadarajah, and C. Ekanayake, “On recent advances in PV output power forecast,” Solar Energy, vol. 136, pp. 125–144, 2016.
13. [13] B. Uzum, A. Onen, H. M. Hasanien, and S. M. Muyeen, “Rooftop solar pv penetration impacts on distribution network and further growth factors— a comprehensive review,” Electronics (Basel), vol. 10, no. 1, p. 55, 2020.
14. [14] G. Mosaico and M. Saviozzi, “A hybrid methodology for the day-ahead PV forecasting exploiting a Clear Sky Model or Artificial Neural Networks,” in IEEE EUROCON 2019-18th International Conference on Smart Technologies, IEEE, 2019, pp. 1–6.
15. [15] S. Sobri, S. Koohi-Kamali, and N. A. Rahim, “Solar photovoltaic generation forecasting methods: A review,” Energy Convers Manag, vol. 156, pp. 459–497, 2018.
16. [16] C. Yang, A. A. Thatte, and L. Xie, “Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation,” IEEE Trans Sustain Energy, vol. 6, no. 1, pp. 104–112, 2014.
17. [17] U. K. Das et al., “Forecasting of photovoltaic power generation and model optimization: A review,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 912–928, 2018.
18. [18] R. Samu et al., “Applications for solar irradiance nowcasting in the control of microgrids: A review,” Renewable and Sustainable Energy Reviews, vol. 147, p. 111187, 2021.
19. [19] M. N. Akhter, S. Mekhilef, H. Mokhlis, and N. M. Shah, “Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques,” IET Renewable Power Generation, vol. 13, no. 7, pp. 1009–1023, 2019.
20. [20] D. W. van der Meer, J. Widén, and J. Munkhammar, “Review on probabilistic forecasting of photovoltaic power production and electricity consumption,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 1484–1512, 2018.
21. [21] R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, “A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization,” Renewable and Sustainable Energy Reviews, vol. 124, p. 109792, 2020.
22. [22] W. Li, H. Ren, P. Chen, Y. Wang, and H. Qi, “Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review,” Energies (Basel), vol. 13, no. 22, p. 5951, 2020.
23. [23] A. Mellit, A. Massi Pavan, E. Ogliari, S. Leva, and V. Lughi, “Advanced methods for photovoltaic output power forecasting: A review,” Applied Sciences, vol. 10, no. 2, p. 487, 2020.
24. [24] A. Nespoli et al., “Day-ahead photovoltaic forecasting: A comparison of the most effective techniques,” Energies (Basel), vol. 12, no. 9, p. 1621, 2019.
25. [25] F. Barbieri, S. Rajakaruna, and A. Ghosh, “Very short-term photovoltaic power forecasting with cloud modeling: A review,” Renewable and Sustainable Energy Reviews, vol. 75, pp. 242–263, 2017.
26. [26] E. Ogliari, A. Dolara, G. Manzolini, and S. Leva, “Physical and hybrid methods comparison for the day ahead PV output power forecast,” Renew Energy, vol. 113, pp. 11–21, 2017.
27. [27] V. Kushwaha and N. M. Pindoriya, “Very short-term solar PV generation forecast using SARIMA model: A case study,” in 2017 7th International Conference on Power Systems (ICPS), IEEE, 2017, pp. 430–435.
28. [28] D. Dissawa, M. P. B. Ekanayake, G. Godaliyadda, J. B. Ekanayake, and A. P. Agalgaonkar, “Cloud motion tracking for short-term on-site cloud coverage prediction,” in 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE, 2017, pp. 1–6.
29. [29] Z. Zhen, F. Wang, Z. Mi, Y. Sun, and H. Sun, “Cloud tracking and forecasting method based on optimization model for PV power forecasting,” in 2015 Australasian Universities Power Engineering Conference (AUPEC), IEEE, 2015, pp. 1–4.
30. [30] V. E. Larson, “Forecasting solar irradiance with numerical weather prediction models,” Solar energy forecasting and resource assessment, pp. 299– 318, 2013.
31. [31] J. L. Bosch and J. Kleissl, “Cloud motion vectors from a network of ground sensors in a solar power plant,” Solar Energy, vol. 95, pp. 13–20, 2013.
32. [32] M. Lave and J. Kleissl, “Cloud speed impact on solar variability scaling–Application to the wavelet variability model,” Solar Energy, vol. 91, pp. 11–21, 2013.
33. [33] C. W. Chow et al., “Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed,” Solar Energy, vol. 85, no. 11, pp. 2881–2893, 2011.
34. [34] T. E. Hoff and R. Perez, “Quantifying PV power output variability,” Solar Energy, vol. 84, no. 10, pp. 1782–1793, 2010.
35. [35] R. Perez, S. Kivalov, J. Schlemmer, K. Hemker Jr, D. Renné, and T. E. Hoff, “Validation of short and medium term operational solar radiation forecasts in the US,” Solar Energy, vol. 84, no. 12, pp. 2161–2172, 2010.
36. [36] Y. Fabel et al., “Applying self-supervised learning for semantic cloud segmentation of all-sky images,” Atmospheric Measurement Techniques Discussions, pp. 1–20, 2021.
37. [37] S. Emeis, C. Münkel, S. Vogt, W. J. Müller, and K. Schäfer, “Atmospheric boundary-layer structure from simultaneous SODAR, RASS, and ceilometer measurements,” Atmos Environ, vol. 38, no. 2, pp. 273–286, 2004.
38. [38] O. E. Hassan and A. K. Abdelsalam, “New Time Horizon Based Classification of PV Power Generation Forecasting Techniques,” in 2020 30th International Conference on Computer Theory and Applications (ICCTA), IEEE, 2020, pp. 88–95.
39. [39] S. R. West, D. Rowe, S. Sayeef, and A. Berry, “Short-term irradiance forecasting using skycams: Motivation and development,” Solar Energy, vol. 110, pp. 188–207, 2014.
40. [40] R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, “A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization,” Renewable and Sustainable Energy Reviews, vol. 124, p. 109792, 2020.
41. [41] M. Pierro et al., “Multi-Model Ensemble for day ahead prediction of photovoltaic power generation,” Solar energy, vol. 134, pp. 132–146, 2016.
42. [42] Y. Zhang, M. Beaudin, R. Taheri, H. Zareipour, and D. Wood, “Day-ahead power output forecasting for small-scale solar photovoltaic electricity generators,” IEEE Trans Smart Grid, vol. 6, no. 5, pp. 2253–2262, 2015.
43. [43] D. P. Larson, L. Nonnenmacher, and C. F. M. Coimbra, “Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest,” Renew Energy, vol. 91, pp. 11–20, 2016.
44. [44] A. Nespoli et al., “Day-ahead photovoltaic forecasting: A comparison of the most effective techniques,” Energies (Basel), vol. 12, no. 9, p. 1621, 2019.
45. [45] X. Qing and Y. Niu, “Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM,” Energy, vol. 148, pp. 461–468, 2018.
46. [46] R. M. Ehsan, S. P. Simon, and P. R. Venkateswaran, “Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron,” Neural Comput Appl, vol. 28, no. 12, pp. 3981–3992, 2017.
47. [47] H.-T. Yang, C.-M. Huang, Y.-C. Huang, and Y.-S. Pai, “A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output,” IEEE Trans Sustain Energy, vol. 5, no. 3, pp. 917–926, 2014.
48. [48] A. Dolara, S. Leva, and G. Manzolini, “Comparison of different physical models for PV power output prediction,” Solar energy, vol. 119, pp. 83– 99, 2015.
49. [49] K. Mohammadi, S. Shamshirband, C. W. Tong, M. Arif, D. Petković, and S. Ch, “A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation,” Energy Convers Manag, vol. 92, pp. 162–171, 2015.
50. [50] S. Bhardwaj et al., “Estimation of solar radiation using a combination of Hidden Markov Model and generalized Fuzzy model,” Solar Energy, vol. 93, pp. 43–54, 2013.
51. [51] L. Olatomiwa, S. Mekhilef, S. Shamshirband, K. Mohammadi, D. Petković, and C. Sudheer, “A support vector machine–firefly algorithm-based model for global solar radiation prediction,” Solar Energy, vol. 115, pp. 632–644, 2015.
52. [52] S. Ghimire, R. C. Deo, N. J. Downs, and N. Raj, “Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities,” Remote Sens Environ, vol. 212, pp. 176– 198, 2018.
53. [53] M. Aslam, J.-M. Lee, H.-S. Kim, S.-J. Lee, and S. Hong, “Deep learning models for long-term solar radiation forecasting considering microgrid installation: A comparative study,” Energies (Basel), vol. 13, no. 1, p. 147, 2020.
54. [54] S. Pelland, G. Galanis, and G. Kallos, “Solar and photovoltaic forecasting through post‐processing of the Global Environmental Multiscale numerical weather prediction model,” Progress in photovoltaics: Research and Applications, vol. 21, no. 3, pp. 284–296, 2013.
55. [55] H. Rezk, I. Tyukhov, and A. Raupov, “Experimental implementation of meteorological data and photovoltaic solar radiation monitoring system,” International Transactions on Electrical Energy Systems, vol. 25, no. 12, pp. 3573–3585, 2015.
56. [56] A. Tuohy et al., “Solar forecasting: methods, challenges, and performance,” IEEE Power and Energy Magazine, vol. 13, no. 6, pp. 50–59, 2015.
57. [57] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
58. [58] C. Wan, J. Zhao, Y. Song, Z. Xu, J. Lin, and Z. Hu, “Photovoltaic and solar power forecasting for smart grid energy management,” CSEE Journal of Power and Energy Systems, vol. 1, no. 4, pp. 38–46, 2015.
59. [59] S. Rajagopalan and S. Santoso, “Wind power forecasting and error analysis using the autoregressive moving average modeling,” in 2009 IEEE Power & Energy Society General Meeting, IEEE, 2009, pp. 1–6.
60. [60] B. E. Hansen, “TIME SERIES ANALYSISJames D. Hamilton Princeton University Press, 1994,” Econ Theory, vol. 11, no. 3, pp. 625–630, 1995.
61. [61] D. Yang, J. Kleissl, C. A. Gueymard, H. T. C. Pedro, and C. F. M. Coimbra, “History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining,” Solar Energy, vol. 168, pp. 60–101, 2018.
62. [62] J. Contreras, R. Espinola, F. J. Nogales, and A. J. Conejo, “ARIMA models to predict next-day electricity prices,” IEEE transactions on power systems, vol. 18, no. 3, pp. 1014–1020, 2003.
63. [63] G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
64. [64] C.-M. Huang, C.-J. Huang, and M.-L. Wang, “A particle swarm optimization to identifying the ARMAX model for short-term load forecasting,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 1126–1133, 2005.
65. [65] R. H. Inman, H. T. C. Pedro, and C. F. M. Coimbra, “Solar forecasting methods for renewable energy integration,” Prog Energy Combust Sci, vol. 39, no. 6, pp. 535–576, 2013.
66. [66] G. Reikard, “Predicting solar radiation at high resolutions: A comparison of time series forecasts,” Solar Energy, vol. 83, no. 3, pp. 342–349, 2009.
67. [67] D. Yang, P. Jirutitijaroen, and W. M. Walsh, “Hourly solar irradiance time series forecasting using cloud cover index,” Solar Energy, vol. 86, no. 12, pp. 3531–3543, 2012.
68. [68] S. Das, “Short term forecasting of solar radiation and power output of 89.6 kWp solar PV power plant,” Mater Today Proc, vol. 39, pp. 1959–1969, 2021.
69. [69] A. W. Aryaputera, D. Yang, and W. M. Walsh, “Day-ahead solar irradiance forecasting in a tropical environment,” J Sol Energy Eng, vol. 137, no. 5, p. 051009, 2015.
70. [70] M. Bouzerdoum, A. Mellit, and A. M. Pavan, “A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant,” Solar Energy, vol. 98, pp. 226–235, 2013.
71. [71] A.-R. Nadia and H. Al-Najjar, “A Comparative Assessment of Time Series Forecasting Using NARX and SARIMA to Predict Hourly, Daily, and Monthly Global Solar Radiation Based on Short-Term Dataset,” Arab J Sci Eng, pp. 1–22, 2021.
72. [72] Y. Li, Y. Su, and L. Shu, “An ARMAX model for forecasting the power output of a grid connected photovoltaic system,” Renew Energy, vol. 66, pp. 78–89, 2014.
73. [73] J. P. González, A. M. S. M. San Roque, and E. A. Perez, “Forecasting functional time series with a new Hilbertian ARMAX model: Application to electricity price forecasting,” IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 545–556, 2017.
74. [74] S. B. Choi and I. Ahn, “Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina,” PLoS One, vol. 15, no. 7, p. e0233855, 2020.
75. [75] X. Li, H. Li, B. Pan, and R. Law, “Machine learning in Internet search query selection for tourism forecasting,” J Travel Res, vol. 60, no. 6, pp. 1213–1231, 2021.
76. [76] X. Zhang, Y. Li, S. Lu, H. F. Hamann, B.-M. Hodge, and B. Lehman, “A solar time based analog ensemble method for regional solar power forecasting,” IEEE Trans Sustain Energy, vol. 10, no. 1, pp. 268–279, 2018.
77. [77] F. Bizzarri, M. Bongiorno, A. Brambilla, G. Gruosso, and G. S. Gajani, “Model of photovoltaic power plants for performance analysis and production forecast,” IEEE Trans Sustain Energy, vol. 4, no. 2, pp. 278–285, 2012.
78. [78] S. S. Soman, H. Zareipour, O. Malik, and P. Mandal, “A review of wind power and wind speed forecasting methods with different time horizons,” in North American Power Symposium 2010, IEEE, 2010, pp. 1–8.
79. [79] D. Lew and R. Piwko, “Western wind and solar integration study,” National Renewable Energy Laboratories, Technical Report No. NREL/SR-550- 47781, 2010.
80. [80] P. Mathiesen and J. Kleissl, “Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States,” Solar Energy, vol. 85, no. 5, pp. 967–977, 2011.
81. [81] E. Lorenz et al., “Benchmarking of different approaches to forecast solar irradiance,” in 24th European photovoltaic solar energy conference, Hamburg Germany, 2009, pp. 21–25.
82. [82] M. Schroedter-Homscheidt and G. Gesell, “Verification of sectoral cloud motion based direct normal irradiance nowcasting from satellite imagery,” in AIP Conference Proceedings, AIP Publishing LLC, 2016, p. 150007.
83. [83] C. Monteiro, T. Santos, L. A. Fernandez-Jimenez, I. J. Ramirez-Rosado, and M. S. Terreros-Olarte, “Short-term power forecasting model for photovoltaic plants based on historical similarity,” Energies (Basel), vol. 6, no. 5, pp. 2624–2643, 2013.
84. [84] F. Antonanzas-Torres, J. Antonanzas, R. Urraca, M. Alia-Martinez, and F. J. Martinez-de-Pison, “Impact of atmospheric components on solar clearsky models at different elevation: Case study Canary Islands,” Energy Convers Manag, vol. 109, pp. 122–129, 2016.
85. [85] C. Rigollier, O. Bauer, and L. Wald, “On the clear sky model of the ESRA—European Solar Radiation Atlas—with respect to the Heliosat method,” Solar energy, vol. 68, no. 1, pp. 33–48, 2000.
86. [86] P. Ineichen, “High turbidity solis clear sky model: development and validation,” Remote Sens (Basel), vol. 10, no. 3, p. 435, 2018.
87. [87] D. J. Gagne II, A. McGovern, S. E. Haupt, and J. K. Williams, “Evaluation of statistical learning configurations for gridded solar irradiance forecasting,” Solar Energy, vol. 150, pp. 383–393, 2017.
88. [88] V. E. Larson, “Forecasting solar irradiance with numerical weather prediction models,” Solar energy forecasting and resource assessment, pp. 299– 318, 2013.89] D. Yang, W. Li, G. M. Yagli, and D. Srinivasan, “Operational solar forecasting for grid integration: Standards, challenges, a nd outlook,” Solar Energy, vol. 224, pp. 930–937, 2021.
89. [90] P. Bacher, H. Madsen, and H. A. Nielsen, “Online short-term solar power forecasting,” Solar energy, vol. 83, no. 10, pp. 1772–1783, 2009.
90. [91] F. J. L. Lima, F. R. Martins, E. B. Pereira, E. Lorenz, and D. Heinemann, “Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks,” Renew Energy, vol. 87, pp. 807–818, 2016.
91. [92] J. Huang, M. Korolkiewicz, M. Agrawal, and J. Boland, “Forecasting solar radiation on an hourly time scale using a Coupled Au toRegressive and Dynamical System (CARDS) model,” Solar Energy, vol. 87, pp. 136–149, 2013.
92. [93] R. W. Mueller et al., “Rethinking satellite-based solar irradiance modelling: The SOLIS clear-sky module,” Remote Sens Environ, vol. 91, no. 2, pp. 160–174, 2004.
93. [94] P. Ineichen, “A broadband simplified version of the Solis clear sky model,” Solar Energy, vol. 82, no. 8, pp. 758–762, 2008.
94. [95] P. Ineichen and R. Perez, “A new airmass independent formulation for the Linke turbidity coefficient,” Solar Energy, vol. 73, no. 3, pp. 151–157, 2002.
95. [96] A. Sözen, E. Arcaklıoğlu, M. Özalp, and N. Çağlar, “Forecasting based on neural network approach of solar potential in Turkey ,” Renew Energy, vol. 30, no. 7, pp. 1075–1090, 2005.
96. [97] H. T. C. Pedro and C. F. M. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs,” Solar Energy, vol. 86, no. 7, pp. 2017–2028, 2012.
97. [98] G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
98. [99] C. Cortes and V. Vapnik, “Support-vector networks,” Mach Learn, vol. 20, no. 3, pp. 273–297, 1995.
99. [100] N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, “Predicting solar generation from weather forecasts using machine learning,” in 2011 IEEE international conference on smart grid communications (SmartGridComm), IEEE, 2011, pp. 528–533.
100. [101] T. Denoeux, “A k-nearest neighbor classification rule based on Dempster-Shafer theory,” in Classic works of the Dempster-Shafer theory of belief functions, Springer, 2008, pp. 737–760.
101. [102] Haupt, T., Trull, O., & Moog, M. (2025). PV production forecast using hybrid models of time series with machine learning methods. Energies, 18(11), 2692.
102. [103] Wan, H., Qiu, Z., Wang, J., Quan, R., Chang, Y., & Derigent, W. (2025). Optimizing renewable energy forecasting: a hybrid approach integrating MSADBO, BiGRU, and TCN for PV/wind power generation prediction. The Journal of Supercomputing, 81(13), 1269.
103. [104] Naprawski, T., Kowalik, J., & Pilarczyk-Naprawski, A. (2025). APPLICATION OF ARTIFICIAL INTELLIGENCE IN BUSINESS PROCESS OPTIMISATION ON THE EXAMPLE OF INTELLIGENT ENERGY PRICE FORECASTING FOR PHOTOVOLTAIC FARMS. Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol. 2, 389-406.
104. [105] Wang, K., Qi, X., & Liu, H. (2019). Photovoltaic power forecasting-based LSTM-Convolutional Network. Energy, 189, 116225.
105. [106] Deng, F., Wang, T., Tao, W., Darkwa, J., & Li, Y. (2025). A LSTM-model based approach for long-term forecasting of high-rise residential building integrated photovoltaic system. Energy, 138784.
106. [107] Liu, H., Du, C., Guo, R., Luo, Y., Cui, Y., Zi, J., ... & Cao, Y. (2026). Short-Term PV Power Forecasting with Temporal-Attention LSTM and Successive-Halving Hyperparameter Search. Electronics, 15(5), 1019.
107. [108] Sushmi, N. B., & Subbulekshmi, D. (2025). Real-time ultra short-term irradiance forecasting using a novel R-GRU model for optimizing PV controller dynamics. Results in Engineering, 26, 105046.
108. [109] Kim, T. G., Yoon, S. G., & Song, K. B. (2025). Very short-term load forecasting model for large power system using GRU-attention algorithm. Energies, 18(13), 3229.