Modeling of Intraday Fluctuations in Future Climate Parameters and Their Impact on Perovskite Solar Modules, from a Sustainable Development Perspective
Modeling of intraday fluctuation in future climate parameters. This study uses ARIMA and LSTM models to evaluate and examine the prediction accuracy of four main meteorological parameters (solar irradiance, temperature, relative humidity, and wind speed) that have an impact on perovskite solar modules using Chad as an example. The findings show that the use of averaged climatic data considerably obscures the effects of intraday fluctuation, which significantly increases power output volatility and speeds up degradation. Increasing short-term temperature extremes may further jeopardize system reliability, according to projections under future climate scenarios. The results highlight the necessity of climate-resilient photovoltaic modeling techniques for long-term technological viability and sustainable energy system design. The significance of national and international policy decisions in shaping Chad’s demographic, economic, and environmental future is highlighted by these Shared Socioeconomic Pathway projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0, and SSP5-8.5). Chad has an important opportunity to pursue a sustainable development pathway that balances economic growth, social equity, and environmental stewardship.
References
[1]
Stoichkov, V., Bristow, N., Troughton, J., De Rossi, F., Watson, T.M. and Kettle, J. (2018) Outdoor Performance Monitoring of Perovskite Solar Cell Mini-Modules: Diurnal Performance, Observance of Reversible Degradation and Variation with Climatic Performance. SolarEnergy, 170, 549-556. https://doi.org/10.1016/j.solener.2018.05.086
[2]
Lipov？ek, B., Jo？t, M., Tom？i？, ？. and Topi？, M. (2022) Energy Yield of Perovskite Solar Cells: Influence of Location, Orientation, and External Light Management. SolarEnergyMaterialsandSolarCells, 234, Article 111421. https://doi.org/10.1016/j.solmat.2021.111421
[3]
Gehlhaar, R., Merckx, T., Qiu, W. and Aernouts, T. (2018) Outdoor Measurement and Modeling of Perovskite Module Temperatures. GlobalChallenges, 2, Article No. 1800008. https://doi.org/10.1002/gch2.201800008
[4]
Mavlonov, A., Hishikawa, Y., Kawano, Y., Negami, T., Hayakawa, A., Tsujimura, S., et al. (2024) Thermal Stability Test on Flexible Perovskite Solar Cell Modules to Estimate Activation Energy of Degradation on Temperature. SolarEnergyMaterialsandSolarCells, 277, Article 113148. https://doi.org/10.1016/j.solmat.2024.113148
[5]
Mavlonov, A., Hishikawa, Y., Kawano, Y., Negami, T., Hayakawa, A. and Minemoto, T. (2025) Investigating the Stability of Flexible Perovskite Solar Cell Modules in Heat and Damp-Heat Environments. SolarEnergyMaterialsandSolarCells, 282, Article 113410. https://doi.org/10.1016/j.solmat.2025.113410
[6]
Kato, Y., Katayama, H., Kobayashi, T., Kozawa, M., Nishigaki, Y., Kobayashi, T., et al. (2022) Global Prediction of the Energy Yields for Hybrid Perovskite/Si Tandem and Si Heterojunction Single Solar Modules. ProgressinPhotovoltaics: ResearchandApplications, 30, 1198-1218. https://doi.org/10.1002/pip.3569
[7]
Bhandari, S., Ghosh, A., Roy, A., Mallick, T.K. and Sundaram, S. (2022) Compelling Temperature Behaviour of Carbon-Perovskite Solar Cell for Fenestration at Various Climates. ChemicalEngineeringJournalAdvances, 10, Article 100267. https://doi.org/10.1016/j.ceja.2022.100267
[8]
Tewari, C., Tatrari, G., Kumar, S., Pandey, S., Rana, A., Pal, M., et al. (2022) Green and Cost-Effective Synthesis of 2D and 3D Graphene-Based Nanomaterials from Drepanostachyum Falcatum for Bio-Imaging and Water Purification Applications. ChemicalEngineeringJournalAdvances, 10, Article 100265. https://doi.org/10.1016/j.ceja.2022.100265
[9]
Kling, M.M., Baer, K.C. and Ackerly, D.D. (2024) A Tree’s View of the Terrain: Downscaling Bioclimate Variables to High Resolution Using a Novel Multi‐Level Species Distribution Model. Ecography, 2024, e07131. https://doi.org/10.1111/ecog.07131
[10]
Guo, J., Jia, H., Wang, Y., Wang, X. and Li, W. (2024) Dynamical Downscaling of Daily Extreme Temperatures over China Using PRECIS Model. Sustainability, 16, Article 3030. https://doi.org/10.3390/su16073030
[11]
Bonato, M., Cepni, O., Gupta, R. and Pierdzioch, C. (2023) Climate Risks and Realized Volatility of Major Commodity Currency Exchange Rates. JournalofFinancialMarkets, 62, Article 100760. https://doi.org/10.1016/j.finmar.2022.100760
[12]
Zhou, Y., Liang, Z., Li, B., Huang, Y., Wang, K. and Hu, Y. (2021) Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model. Sustainability, 13, Article 3588. https://doi.org/10.3390/su13063588
[13]
Singh, S. and Goyal, M.K. (2023) Enhancing Climate Resilience in Businesses: The Role of Artificial Intelligence. JournalofCleanerProduction, 418, Article 138228. https://doi.org/10.1016/j.jclepro.2023.138228
[14]
Plavcová, L., Tumajer, J., Altman, J., Svoboda, M., Stegehuis, A.I., Pejcha, V., et al. (2025) High Inter‐Specific Diversity and Seasonality of Trunk Radial Growth in Trees along an Afrotropical Elevational Gradient. Plant, Cell&Environment, 48, 2285-2297. https://doi.org/10.1111/pce.15295
[15]
Alizadeh, O. (2022) Advances and Challenges in Climate Modeling. ClimaticChange, 170, Article No. 18. https://doi.org/10.1007/s10584-021-03298-4
[16]
Chakraborty, D., Alam, A., Chaudhuri, S., Ba？a？ao？lu, H., Sulbaran, T. and Langar, S. (2021) Scenario-Based Prediction of Climate Change Impacts on Building Cooling Energy Consumption with Explainable Artificial Intelligence. AppliedEnergy, 291, Article 116807. https://doi.org/10.1016/j.apenergy.2021.116807
[17]
Xu, Z., Chen, L., Qin, P. and Ji, X. (2023) Projection and Uncertainty Analysis of Future Temperature Change over the Yarlung Tsangpo-Brahmaputra River Basin Based on Cmip6. Water, 15, Article 3595. https://doi.org/10.3390/w15203595
[18]
Aryal, S., Grie？inger, J., Dyola, N., Gaire, N.P., Bhattarai, T. and Br？uning, A. (2023) INTRAGRO: A Machine Learning Approach to Predict Future Growth of Trees under Climate Change. EcologyandEvolution, 13, e10626. https://doi.org/10.1002/ece3.10626
[19]
Tom？i？, ？., Jo？t, M., Brecl, K., Topi？, M. and Lipov？ek, B. (2023) Energy Yield Modeling for Optimization and Analysis of Perovskite‐Silicon Tandem Solar Cells under Realistic Outdoor Conditions. AdvancedTheoryandSimulations, 6, Article No. 2200931. https://doi.org/10.1002/adts.202200931
[20]
Zhao, Y. and Liu, S. (2023) Effects of Climate Change on Economic Growth: A Perspective of the Heterogeneous Climate Regions in Africa. Sustainability, 15, Article 7136. https://doi.org/10.3390/su15097136
[21]
Liu, J., Ma, X., Duan, Z., Jiang, J., Reichstein, M. and Jung, M. (2020) Impact of Temporal Precipitation Variability on Ecosystem Productivity. WIREsWater, 7, e1481. https://doi.org/10.1002/wat2.1481
[22]
Carvalho, D., Cardoso Pereira, S. and Rocha, A. (2021) Future Surface Temperatures over Europe According to CMIP6 Climate Projections: An Analysis with Original and Bias-Corrected Data. ClimaticChange, 167, Article No. 10. https://doi.org/10.1007/s10584-021-03159-0
[23]
Shu, Q., Wang, Q., ？rthun, M., Wang, S., Song, Z., Zhang, M., et al. (2022) Arctic Ocean Amplification in a Warming Climate in CMIP6 Models. ScienceAdvances, 8, eabn9755. https://doi.org/10.1126/sciadv.abn9755
[24]
Opoku, S.K., Filho, W.L., Hubert, F. and Adejumo, O. (2021) Climate Change and Health Preparedness in Africa: Analysing Trends in Six African Countries. InternationalJournalofEnvironmentalResearchandPublicHealth, 18, Article 4672. https://doi.org/10.3390/ijerph18094672
[25]
Zhu, X., Qu, F., Fan, R., Chen, Z., Wang, Q. and Yu, G. (2022) Effects of Ecosystem Types on the Spatial Variations in Annual Gross Primary Productivity over Terrestrial Ecosystems of China. ScienceofTheTotalEnvironment, 833, Article 155242. https://doi.org/10.1016/j.scitotenv.2022.155242
[26]
Maclean, I.M.D. (2019) Predicting Future Climate at High Spatial and Temporal Resolution. GlobalChangeBiology, 26, 1003-1011. https://doi.org/10.1111/gcb.14876
[27]
Tebaldi, C., Debeire, K., Eyring, V., et al. (2021) Climate Model Projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth System Dynamics, 12, 253-293.
[28]
Dosio, A., Jury, M.W., Almazroui, M., Ashfaq, M., Diallo, I., Engelbrecht, F.A., et al. (2021) Projected Future Daily Characteristics of African Precipitation Based on Global (CMIP5, CMIP6) and Regional (CORDEX, CORDEX-CORE) Climate Models. ClimateDynamics, 57, 3135-3158. https://doi.org/10.1007/s00382-021-05859-w
[29]
Liu, J., He, Y., Ding, L., Zhang, H., Li, Q., Jia, L., et al. (2024) Perovskite/Silicon Tandem Solar Cells with Bilayer Interface Passivation. Nature, 635, 596-603. https://doi.org/10.1038/s41586-024-07997-7
[30]
Hou, Y., Aydin, E., De Bastiani, M., Xiao, C., Isikgor, F.H., Xue, D., et al. (2020) Efficient Tandem Solar Cells with Solution-Processed Perovskite on Textured Crystalline Silicon. Science, 367, 1135-1140. https://doi.org/10.1126/science.aaz3691
[31]
Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., et al. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization. Geoscientific Model Development, 9, 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016
[32]
Blazakis, K., Schetakis, N., Bonfini, P., Stavrakakis, K., Karapidakis, E. and Katsigiannis, Y. (2024) Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting. Sensors, 24, Article 5035. https://doi.org/10.3390/s24155035
[33]
Xu, D., Zhang, Q., Ding, Y. and Zhang, D. (2022) Application of a Hybrid ARIMA-LSTM Model Based on the SPEI for Drought Forecasting. EnvironmentalScienceandPollutionResearch, 29, 4128-4144. https://doi.org/10.1007/s11356-021-15325-z
[34]
Chen, H., Su, Z. and Xing, Z. (2024) Forecasting Extreme High Temperatures Based on an ARIMA-LSTM Hybrid Model. 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA), Shenyang, 28-30 June 2024, 804-809. https://doi.org/10.1109/icipca61593.2024.10709045
