,
Mamadou Traore*,
Daouda Gueye,
Amadou Sidibe,
Amadou Ba,
Cheikh Saliou Toure,
Alphousseyni Ndiaye,
Senghane Mbodji,
West Africa benefits from strong solar resources, yet in Guinea the influence of climate change on photovoltaic availability has received little attention. This work evaluates the effects of climate change on the photovoltaic potential of Kankan (Guinea) using daily data from the NASA POWER database for 2000-2025. The study focuses on global solar irradiation, air temperature, relative humidity, and wind speed. A semi-empirical model is used to estimate daily photovoltaic potential, and trends are analyzed with linear regression and the non-parametric Mann-Kendall test. In parallel, a multilayer perceptron artificial neural network (MPANN) is developed to reproduce the estimated photovoltaic potential from the selected climatic inputs. The analysis shows significant declines in air temperature (-0.0559°C year-1) and wind speed (-0.0077 ms-1year-1), together with a significant rise in relative humidity (+0.6249% year-1). Global solar irradiation presents no statistically significant trend over the study period. Despite these changes, the photovoltaic potential remains broadly stable, with a small and statistically non-significant decrease of -0.0152 kWh day-1year-1 (p = 0.186 > 0.05). The MPANN delivers very high predictive performance, with the coefficient of determination of 0.99, the mean absolute error (MAE) of 0.0266 kWh day-1, the root mean square error (RMSE) of 0.0478 kWh day-1, and the mean absolute percentage error (MAPE) of 0.134%. Overall, the climatic shifts observed in Kankan during the last twenty-six years have not significantly altered the region’s photovoltaic potential. This stability supports the suitability of solar energy as a sustainable option to meet Guinea’s growing energy needs. The study also highlights the usefulness of artificial intelligence methods for assessing and forecasting renewable energy resources in tropical regions.
| Published in | Science Journal of Energy Engineering (Volume 14, Issue 3) |
| DOI | 10.11648/j.sjee.20261403.12 |
| Page(s) | 83-97 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Climate Variability, Photovoltaic Potential, Artificial Neural Network, NASA POWER, Mann–Kendall Test, Kankan, Guinea
| [1] | K. Calvin et al., « IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. », Intergovernmental Panel on Climate Change (IPCC), juill. 2023. |
| [2] | L. Cozzi et T. Gould, « World Energy Outlook 2023 ». International Energy Agency, France. [En ligne]. Disponible sur: |
| [3] | D. Gielen, F. Boshell, D. Saygin, M. D. Bazilian, N. Wagner, et R. Gorini, « The role of renewable energy in the global energy transformation », Energy Strategy Rev., vol. 24, p. 38‑50, avr. 2019, |
| [4] | « RENEWABLES 2023 GLOBAL STATUS REPORT ». [En ligne]. Disponible sur: |
| [5] | « IRENA (2025), Renewable power generation costs in 2024, International Renewable Energy Agency, Abu Dhabi. » [En ligne]. Disponible sur: |
| [6] |
D. S. Philipps, F. Ise, W. Warmuth, et P. P. GmbH, « Photovoltaics Report », juin 2026, [En ligne]. Disponible sur:
https://www.ise.fraunhofer.de/de/veroeffentlichungen/studien/photovoltaics-report.html |
| [7] | T. Alex-Oke et al., « Renewable energy market in Africa: Opportunities, progress, challenges, and future prospects », Energy Strategy Rev., vol. 59, p. 101700, mai 2025, |
| [8] | P. A. Owusu et S. Asumadu-Sarkodie, « A review of renewable energy sources, sustainability issues and climate change mitigation », Cogent Eng., vol. 3, no 1, p. 1167990, dec. 2016, |
| [9] | E. Skoplaki et J. A. Palyvos, « On the temperature dependence of photovoltaic module electrical performance: A review of efficiency/power correlations », Sol. Energy, vol. 83, no 5, p. 614‑624, mai 2009, |
| [10] | D. S. Hasan, Mansour S. Farhan, et H. Alrikabi, « Impact of Cloud, Rain, Humidity, and Wind Velocity on PV Panel Performance », Wasit J. Eng. Sci., vol. 10, no 2, p. 34‑43, juin 2022, |
| [11] | H. A. Kazem, S. Al-Bahri, S. Al-Badi, H. Al-Mahkladi, et A. H. A. Al-Waeli, « Dust Effect on the Performance of Photovoltaic », Adv. Mater. Res., vol. 875‑877, p. 1908‑1911, fevr. 2014, |
| [12] | M. Erdenebat et S. Khayankhyarvaa, « Influence of Climatic Factors Affecting Photovoltaic System », vol. 10, no 6, 2023. |
| [13] | S. Jerez et al., « The impact of climate change on photovoltaic power generation in Europe », Nat. Commun., vol. 6, no 1, p. 10014, dec. 2015, |
| [14] | M. Wild, D. Folini, F. Henschel, N. Fischer, et B. Müller, « Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems », Sol. Energy, vol. 116, p. 12‑24, juin 2015, |
| [15] | C. Nichols, M. Hill, et X. Liu, « Climate Change Impacts on Solar Energy Generation in the Continental United States, Forecasts from Deep Learning », 2024, SSRN. |
| [16] | T. AlSkaif, « A systematic analysis of meteorological variables for PV output power estimation », Renew. Energy, vol. 153, no 12‑22, 2020, |
| [17] | P. Sarmah et al., « Comprehensive Analysis of Solar Panel Performance and Correlations with Meteorological Parameters », ACS Omega, vol. 8, no 50, p. 47897‑47904, dec. 2023, |
| [18] | A. Bichet, B. Hingray, G. Evin, A. Diedhiou, C. M. F. Kebe, et S. Anquetin, « Potential impact of climate change on solar resource in Africa for photovoltaic energy: analyses from CORDEX-AFRICA climate experiments », Environ. Res. Lett., vol. 14, no 12, p. 124039, dec. 2019, |
| [19] | D. K. Danso et al., « A CMIP6 assessment of the potential climate change impacts on solar photovoltaic energy and its atmospheric drivers in West Africa », Environ. Res. Lett., vol. 17, no 4, p. 044016, avr. 2022, |
| [20] | M. Agbor et al., « Effects of Angstrom-Prescott and Hargreaves-Samani Coefficients on Climate Forcing and Solar PV Technology Selection in West Africa », Trends Renew. Energy, vol. 9, no 1, p. 78‑106, mars 2023, |
| [21] | Y. E. Alami et al., « Numerical exploration and relational impact ranking of critical parameters in 3D PV module studies », Results Eng., vol. 27, p. 107097, sept. 2025, |
| [22] | E. H. Honningdalsnes, E. S. Marstein, M. M. Nygård, M. S. Wiig, et H. N. Riise, « Benchmarking irradiation models for photovoltaic applications: A comparative analysis of radiance-based tools », Sol. Energy, vol. 296, p. 113566, août 2025, |
| [23] | M. Yang, H. Zhang, X. Yu, A. S. Seklouli, A. Bouras, et Y. Ouzrout, « A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework », Int. J. Electr. Power Energy Syst., vol. 169, p. 110660, août 2025, |
| [24] | M. Agoundedemba, C. K. Kim, et H.-G. Kim, « Energy Status in Africa: Challenges, Progress and Sustainable Pathways », Energies, vol. 16, no 23, p. 7708, nov. 2023, |
| [25] | « Pays : Republique de Guinee Evaluation et Analyse des Gaps par rapport aux objectifs de SE4ALL ». [En ligne]. Disponible sur: |
| [26] | N. Diaby, M. A. Camara, S. Soumah, et A. Sakouvogui, « Experimental Assessment of Solar Irradiance in Kankan and Validation of a Photovoltaic Resource Prediction Model », Open J. Appl. Sci., vol. 16, no 05, p. 1793‑1811, 2026, |
| [27] | « NASA. (2023). NASA POWER Data Access Viewer. NASA Langley Research Center. » Consulte le: 16 juin 2026. [En ligne]. Disponible sur: |
| [28] | V. A. Jimenez, A. Barrionuevo, A. Will, et S. Rodríguez, « Neural Network for Estimating Daily Global Solar Radiation Using Temperature, Humidity and Pressure as Unique Climatic Input Variables », Smart Grid Renew. Energy, vol. 07, no 03, p. 94‑103, 2016, |
| [29] | A. Mellit et S. A. Kalogirou, « Artificial intelligence techniques for photovoltaic applications: A review », Prog. Energy Combust. Sci., vol. 34, no 5, p. 574‑632, oct. 2008, |
| [30] | N. Premalatha et A. Valan Arasu, « Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms », J. Appl. Res. Technol., vol. 14, no 3, p. 206‑214, juin 2016, |
| [31] | J. A. Duffie et W. A. Beckman, Solar Engineering of Thermal Processes, 1re ed. Wiley, 2013. |
| [32] | H. B. Mann, « Nonparametric Tests Against Trend », Econometrica, vol. 13, no 3, p. 245, juill. 1945, |
| [33] | B. Y. Menna et D. K. Waktola, « Extreme temperature trend and return period mapping in a changing climate in Upper Tekeze river basin, Northern Ethiopia », Phys. Chem. Earth Parts ABC, vol. 128, p. 103234, dec. 2022, |
| [34] | I. Dabanli, E. Şişman, Y. S. Güçlü, M. E. Birpınar, et Z. Şen, « Climate change impacts on sea surface temperature (SST) trend around Turkey seashores », Acta Geophys., vol. 69, no 1, p. 295‑305, fevr. 2021, |
| [35] | Y. J. Then et S. Abdul Halim, « Modified Mann-Kendall with higher-order statistics for trend analysis », Sci. Rep., vol. 16, no 1, p. 500, dec. 2025, |
| [36] | E. Aksay, E. Çoban, et Y. S. Güçlü, « Normalized innovative trend analysis model and Mann-Kendall test for solar data », Model. Earth Syst. Environ., vol. 11, no 5, p. 347, oct. 2025, |
| [37] | C. Banes et al., « Developpement d’un modèle semi-analytique pour la prediction du comportement thermique et energetique d’un panneau photovoltaïque en conditions reelles de fonctionnement », 2024, |
| [38] | T. Chai et R. R. Draxler, « Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature », Geosci. Model Dev., vol. 7, no 3, p. 1247‑1250, juin 2014, |
| [39] | H. Verbois, Y.-M. Saint-Drenan, A. Thiery, et P. Blanc, « Statistical learning for NWP post-processing: A benchmark for solar irradiance forecasting », Sol. Energy, vol. 238, p. 132‑149, mai 2022, |
| [40] | T. Laepple et al., « Regional but not global temperature variability underestimated by climate models at supradecadal timescales », Nat. Geosci., vol. 16, no 11, p. 958‑966, nov. 2023, |
| [41] | C. Deser, A. Phillips, V. Bourdette, et H. Teng, « Uncertainty in climate change projections: the role of internal variability », Clim. Dyn., vol. 38, no 3‑4, p. 527‑546, fevr. 2012, |
| [42] | Intergovernmental Panel On Climate Change, Ed., « Climate Phenomena and their Relevance for Future Regional Climate Change », in Climate Change 2013 – The Physical Science Basis, 1re ed., Cambridge University Press, 2014, p. 1217‑1308. |
| [43] | D. Ospina et al., «Ten New Insights in Climate Science 2025», |
| [44] | P. M. M. Soares, M. C. Brito, et J. A. M. Careto, « Persistence of the high solar potential in Africa in a changing climate », Environ. Res. Lett., vol. 14, no 12, p. 124036, dec. 2019, |
| [45] | D. Faiman, « Assessing the outdoor operating temperature of photovoltaic modules », Prog. Photovolt. Res. Appl., vol. 16, no 4, p. 307‑315, juin 2008, |
| [46] | P. Adigun, K. Dairaku, A. T. Ogunrinde, et X. Xue, « Climate-driven synchronization of solar extremes threatens the resilience of Africa’s regional power pool », Npj Clean Energy, vol. 2, no 1, p. 11, mai 2026, |
| [47] | S. Obahoundje et al., « Teal-WCA: A Climate Services Platform for Planning Solar Photovoltaic and Wind Energy Resources in West and Central Africa in the Context of Climate Change », Data, vol. 9, no 12, p. 148, dec. 2024, |
| [48] | R. Asghar, F. R. Fulginei, M. Quercio, et A. Mahrouch, « Artificial Neural Networks for Photovoltaic Power Forecasting: A Review of Five Promising Models », IEEE Access, vol. 12, p. 90461‑90485, 2024, |
| [49] | T. I. Ingo, L. Gyoh, Y. Sheng, M. K. Kaymak, A. D. Şahin, et H. M. Pouran, « Accelerating the Low-Carbon Energy Transition in Sub-Saharan Africa through Floating Photovoltaic Solar Farms », Atmosphere, vol. 15, no 6, p. 653, mai 2024, |
APA Style
Sow, P. L. T., Traore, M., Gueye, D., Sidibe, A., Ba, A., et al. (2026). Assessment of the Impact of Climatic Variations on the Photovoltaic Potential of Kankan (Guinea) Using NASA POWER Data and Artificial Neural Network (2000–2025). Science Journal of Energy Engineering, 14(3), 83-97. https://doi.org/10.11648/j.sjee.20261403.12
ACS Style
Sow, P. L. T.; Traore, M.; Gueye, D.; Sidibe, A.; Ba, A., et al. Assessment of the Impact of Climatic Variations on the Photovoltaic Potential of Kankan (Guinea) Using NASA POWER Data and Artificial Neural Network (2000–2025). Sci. J. Energy Eng. 2026, 14(3), 83-97. doi: 10.11648/j.sjee.20261403.12
AMA Style
Sow PLT, Traore M, Gueye D, Sidibe A, Ba A, et al. Assessment of the Impact of Climatic Variations on the Photovoltaic Potential of Kankan (Guinea) Using NASA POWER Data and Artificial Neural Network (2000–2025). Sci J Energy Eng. 2026;14(3):83-97. doi: 10.11648/j.sjee.20261403.12
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Download@article{10.11648/j.sjee.20261403.12,
author = {Papa Lat Tabara Sow and Mamadou Traore and Daouda Gueye and Amadou Sidibe and Amadou Ba and Cheikh Saliou Toure and Vone Beavogui and Alphousseyni Ndiaye and Senghane Mbodji and Faoro Eugene Maomou},
title = {Assessment of the Impact of Climatic Variations on the Photovoltaic Potential of Kankan (Guinea) Using NASA POWER Data and Artificial Neural Network (2000–2025)},
journal = {Science Journal of Energy Engineering},
volume = {14},
number = {3},
pages = {83-97},
doi = {10.11648/j.sjee.20261403.12},
url = {https://doi.org/10.11648/j.sjee.20261403.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.20261403.12},
abstract = {West Africa benefits from strong solar resources, yet in Guinea the influence of climate change on photovoltaic availability has received little attention. This work evaluates the effects of climate change on the photovoltaic potential of Kankan (Guinea) using daily data from the NASA POWER database for 2000-2025. The study focuses on global solar irradiation, air temperature, relative humidity, and wind speed. A semi-empirical model is used to estimate daily photovoltaic potential, and trends are analyzed with linear regression and the non-parametric Mann-Kendall test. In parallel, a multilayer perceptron artificial neural network (MPANN) is developed to reproduce the estimated photovoltaic potential from the selected climatic inputs. The analysis shows significant declines in air temperature (-0.0559°C year-1) and wind speed (-0.0077 ms-1year-1), together with a significant rise in relative humidity (+0.6249% year-1). Global solar irradiation presents no statistically significant trend over the study period. Despite these changes, the photovoltaic potential remains broadly stable, with a small and statistically non-significant decrease of -0.0152 kWh day-1year-1 (p = 0.186 > 0.05). The MPANN delivers very high predictive performance, with the coefficient of determination of 0.99, the mean absolute error (MAE) of 0.0266 kWh day-1, the root mean square error (RMSE) of 0.0478 kWh day-1, and the mean absolute percentage error (MAPE) of 0.134%. Overall, the climatic shifts observed in Kankan during the last twenty-six years have not significantly altered the region’s photovoltaic potential. This stability supports the suitability of solar energy as a sustainable option to meet Guinea’s growing energy needs. The study also highlights the usefulness of artificial intelligence methods for assessing and forecasting renewable energy resources in tropical regions.},
year = {2026}
}
TY - JOUR T1 - Assessment of the Impact of Climatic Variations on the Photovoltaic Potential of Kankan (Guinea) Using NASA POWER Data and Artificial Neural Network (2000–2025) AU - Papa Lat Tabara Sow AU - Mamadou Traore AU - Daouda Gueye AU - Amadou Sidibe AU - Amadou Ba AU - Cheikh Saliou Toure AU - Vone Beavogui AU - Alphousseyni Ndiaye AU - Senghane Mbodji AU - Faoro Eugene Maomou Y1 - 2026/09/24 PY - 2026 N1 - https://doi.org/10.11648/j.sjee.20261403.12 DO - 10.11648/j.sjee.20261403.12 T2 - Science Journal of Energy Engineering JF - Science Journal of Energy Engineering JO - Science Journal of Energy Engineering SP - 83 EP - 97 PB - Science Publishing Group SN - 2376-8126 UR - https://doi.org/10.11648/j.sjee.20261403.12 AB - West Africa benefits from strong solar resources, yet in Guinea the influence of climate change on photovoltaic availability has received little attention. This work evaluates the effects of climate change on the photovoltaic potential of Kankan (Guinea) using daily data from the NASA POWER database for 2000-2025. The study focuses on global solar irradiation, air temperature, relative humidity, and wind speed. A semi-empirical model is used to estimate daily photovoltaic potential, and trends are analyzed with linear regression and the non-parametric Mann-Kendall test. In parallel, a multilayer perceptron artificial neural network (MPANN) is developed to reproduce the estimated photovoltaic potential from the selected climatic inputs. The analysis shows significant declines in air temperature (-0.0559°C year-1) and wind speed (-0.0077 ms-1year-1), together with a significant rise in relative humidity (+0.6249% year-1). Global solar irradiation presents no statistically significant trend over the study period. Despite these changes, the photovoltaic potential remains broadly stable, with a small and statistically non-significant decrease of -0.0152 kWh day-1year-1 (p = 0.186 > 0.05). The MPANN delivers very high predictive performance, with the coefficient of determination of 0.99, the mean absolute error (MAE) of 0.0266 kWh day-1, the root mean square error (RMSE) of 0.0478 kWh day-1, and the mean absolute percentage error (MAPE) of 0.134%. Overall, the climatic shifts observed in Kankan during the last twenty-six years have not significantly altered the region’s photovoltaic potential. This stability supports the suitability of solar energy as a sustainable option to meet Guinea’s growing energy needs. The study also highlights the usefulness of artificial intelligence methods for assessing and forecasting renewable energy resources in tropical regions. VL - 14 IS - 3 ER -
Materials and Laser of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal
Materials and Laser of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal;Departement of Physics, University of Julius Nyerere of Kankan, Kankan, Guinea;Efficiency and Energetic System of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal
Efficiency and Energetic System of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal
Departement of Physics, University of Julius Nyerere of Kankan, Kankan, Guinea
Efficiency and Energetic System of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal
Materials and Laser of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal;Department of Photovoltaic Energy, University of Labe, Labe, Guinea
Efficiency and Energetic System of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal
Materials and Laser of Department of Physics, Alioune Diop University of Bambey, Bambey, Senegal
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