A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition

Authors

  • Mokhtar Ali
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Abdelkerim Souahlia
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Abdelhalim Rabehi
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Mawloud Guermoui
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria; Unite de Recherche Appliquee en Energies Renouvelables, URAER, Centre de Developpement des Energies Renouvelables, CDER, 47133, Ghardaia, Algeria
  • Ali Teta
    Applied Automation and Industrial Diagnostics Laboratory (LAADI), Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria
  • Imad Eddine Tibermacine
    Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185, Rome, Italy
  • Abdelaziz Rabehi
    Laboratory of Telecommunication and Smart Systems (LTSS), Faculty of Science and Technology, University of Djelfa, PO Box 3117, Djelfa 17000, Algeria
  • Mohamed Benghanem
    Physics Department, Faculty of Science, Islamic University of Madinah, Madinah, 42351, Saudi Arabia

Keywords:

PV power, Renewable energy, Feature selection, Artificial Neural Networks, Forecasting

Abstract

Accurate forecasting of photovoltaic (PV) power is essential for effective grid integration and energy management, particularly in solar-rich regions such as Algeria. This study presents a robust forecasting framework that combines advanced feature selection techniques with deep learning architectures---namely MLP, GRU, LSTM, BiLSTM, and CNN---to enhance daily PV power prediction accuracy. Three feature selection methods---ReliefF, Minimum Correlation, and Minimum Redundancy Maximum Relevance (MRMR)---are employed to identify the most relevant input variables from a dataset collected in the Ghardaia region. Among the selected predictors, Global Solar Radiation (GSR) consistently proves to be the most influential. To further enhance model inputs, Variational Mode Decomposition (VMD) is applied to extract informative Intrinsic Mode Functions (IMFs) from the selected features. A comparative evaluation of the models indicates that recurrent neural networks, particularly GRU and LSTM, deliver superior performance across various metrics, including RMSE, MAE, nRMSE, nMAE, R², and the correlation coefficient. The GRU model achieves the best results, with an RMSE of 3.246 and an R² of 0.9550 using five IMFs. These findings highlight the effectiveness of integrating optimal feature selection, signal decomposition, and deep learning for reliable PV power forecasting. The proposed hybrid approach provides a practical and scalable solution for enhancing energy planning and operational efficiency in high-solar-potential regions.

Dimensions

[1] M. W. Bouabdelli, F. Rogti, M. Maache & A. Rabehi, “Performance enhancement of CIGS thin-film solar cell, Optik (Stuttg)”, 216 (2020) 164948. https://doi.org/10.1016/j.ijleo.2020.164948.

[2] M. Guermoui & A. Rabehi, “Soft computing for solar radiation potential assessment in Algeria”, International Journal of Ambient Energy 41 (2020) 1524. https://doi.org/10.1080/01430750.2018.1517686.

[3] F. Bendelala, A. Bellakhdar, O. Baitiche, A. Cheknane, M.H.S. Helal, H.S. Hilal & A. Rabehi, “FDTD Modelling of Nanostructured Hemispherical Plasmonic Light Trapping for Enhanced Ultra-thin GaSb TPV Cell”, Semiconductors 59 (2025) 404. https://doi.org/10.1134/S1063782624603029.

[4] M. Suri, J. Betak, K. Rosina, D. Chrkavy, N. Suriova, T. Cebecauer, M. Caltik & B. Erdelyi, “Global Photovoltaic Power Potential by Country (English)”, 2020. [Online]. https://documents1.worldbank.org/curated/en/466331592817725242/pdf/Global-Photovoltaic-Power-Potential-by-Country.pdf.

[5] M. Guermoui, F. Melgani & C. Danilo, “Multi-step ahead forecasting of daily global and direct solar radiation: A review and case study of Ghardaia regi”, J Clean Prod 201 (2018) 716. https://doi.org/10.1016/j.jclepro.2018.08.006.

[6] A. Mehallou, B. M’hamdi, A. Amari, M. Teguar, A. Rabehi, M. Guermoui, A. H. Alharbi, E.-S. M. El-kenawy & D. S. Khafaga, “Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria”, Sci Rep 15 (2025) 4173. https://doi.org/10.1038/s41598-025-88438-x.

[7] A. Belaid, A. Filali, S. Hassani, T. Arrif, M. Guermoui, A. Gama, M. Bouakba, “Heliostat field optimization and comparisons between biomimetic spiral and radial-staggered layouts for different heliostat shapes”, Solar Energy 238 (2022) 162. https://doi.org/10.1016/j.solener.2022.04.035.

[8] H. Bentegri, M. Rabehi, S. Kherfane, T. A. Nahool, A. Rabehi, M. Guermoui, A. A. Alhussan, D. S. Khafaga, M. M. Eid, E.-S.M. El-Kenawy, “Assessment of compressive strength of eco-concrete reinforced using machine learning tools”, Sci Rep 15 (2025) 5017. https://doi.org/10.1038/s41598-025-89530-y.

[9] M. Mahmoud Al Rahhal, M. L. Mekhalfi, M. Guermoui, E. Othman, B. Lei & A. Mahmood, “A dense phase descriptor for human ear recognition”, IEEE Access 6 (2018) 11883. https://doi.org/10.1109/ACCESS.2018.2810339.

[10] M. Guermoui, M. L. Mekhalfi & K. Ferroudji, “Heart sounds analysis using wavelets responses and support vector machines, in: 2013 8th International Workshop on Systems”, Signal Processing and their Applications (WoSSPA), IEEE, Algiers, Algeria, 2013, pp. 233–238. https://doi.org/10.1109/WoSSPA.2013.6602368.

[11] M. Guermoui, T. Arrif, A. Belaid, S. Hassaniv & N. Bailek, “Enhancing direct Normal solar Irradiation forecasting for heliostat field applications through a novel hybrid model”, Energy Convers Manag 304 (2024) 118189. https://doi.org/10.1016/j.enconman.2024.118189.

[12] C. Wan, J. Zhao, Y. Song, Z. Xu, J. Lin & Z. Hu, “Photovoltaic and solar power forecasting for smart grid energy management”, CSEE Journal of Power and Energy Systems 1 (2015) 38. https://doi.org/10.17775/CSEEJPES.2015.00046.

[13] J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F.J. Martinez-de-Pison, F. Antonanzas-Torres, Review of photovoltaic power forecasting, Solar Energy 136 (2016) 78. https://doi.org/10.1016/j.solener.2016.06.069.

[14] D. W. van der Meer, J. Widen & J. Munkhammar, “Review on proba-´ bilistic forecasting of photovoltaic power production and electricity consumption”, Renewable and Sustainable Energy Reviews 81 (2018) 1484. https://doi.org/10.1016/j.rser.2017.05.212.

[15] X. Huang, H. Wang & X. Li, “A multi-scale semantic feature fusion method for remote sensing crop classification”, Comput Electron Agric 224 (2024) 109185. https://doi.org/10.1016/j.compag.2024.109185.

[16] K. Kira & L. A. Rendell, “A Practical Approach to Feature Selection”, Machine Learning Proceedings 1992 (1992) 249. https://doi.org/10.1016/B978-1-55860-247-2.50037-1.

[17] A. Chennana, A. C. Megherbi, N. Bessous, S. Sbaa, A. Teta, E. O. Belabbaci, A. Rabehi, M. Guermoui & T. F. Agajie, “Vibration signal analysis for rolling bearings faults diagnosis based on deepshallow features fusion”, Sci Rep 15 (2025) 9270. https://doi.org/10.1038/s41598-025-93133-y.

[18] R. Khelifi, T. Chekifi, A. Belaid, M. Guermoui, A. Rabehi, F. Khaled, M. Adouane, A. Al-Qattan & T. F. Agajie, “Comparative performance analysis of hemispherical solar stills using date and olive kernels as heat storage material”, Sci Rep 15 (2025) 7128. https://doi.org/10.1038/s41598-025-87448-z

[19] I. E. Tibermacine, A. Douara, M. Guermoui, A. Rabehi, O. Baitiche, A. Boualleg, E. Mehallel & S. Boukredine, “Enhanced Performance of Microstrip Antenna Arrays through Concave Modifications and Cut-Corner Techniques”, ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA 11 (2025) 1414. https://doi.org/10.5935/jetia.v11i51.1414.

[20] H. Peng, F. Long & C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy”, IEEE Trans Pattern Anal Mach Intell 27 (2005) 1226. https://doi.org/10.1109/TPAMI.2005.159.

[21] B. Ladjal, I. E. Tibermacine, M. Bechouat, M. Sedraoui, C. Napoli, A. Rabehi & D. Lalmi, “Hybrid models for direct normal irradiance forecasting: a case study of Ghardaia zone (Algeria)”, Natural Hazards 120 (2024) 14703. https://doi.org/10.1007/s11069-024-06837-1.

[22] P. Bacher, H. Madsen & H. A. Nielsen, “Online short-term solar power forecasting”, Solar Energy 83 (2009) 1772. https://doi.org/10.1016/j.solener.2009.05.016.

[23] E. Lorenz, T. Scheidsteger, J. Hurka, D. Heinemann & C. Kurz, “Regional PV power prediction for improved grid integration”, Progress in Photovoltaics: Research and Applications 19 (2011) 757. https://doi.org/10.1002/pip.1033.

[24] R. H. Inman, H. T. C. Pedro & C. F. M. Coimbra, “Solar forecasting methods for renewable energy integration”, Prog Energy Combust Sci 39 (2013) 535. https://doi.org/10.1016/j.pecs.2013.06.002.

[25] L. A. Fernandez-Jimenez, A. Munoz-Jimenez, A. Falces, M. Mendoza-˜ Villena, E. Garcia-Garrido, P. M. Lara-Santillan, E. Zorzano-Alba & P. J. Zorzano-Santamaria, “Short-term power forecasting system for photovoltaic plants”, Renew Energy 44 (2012) 311. https://doi.org/10.1016/j.renene.2012.01.108.

[26] P. Mathiesen, J. Kleissl, Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States, Solar Energy 85 (2011) 967. https://doi.org/10.1016/j.solener.2011.02.013.

[27] F. J. L. Lima, F. R. Martins, E. B. Pereira, E. Lorenz & D. Heinemann, “Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks”, Renew Energy 87 (2016) 807. https://doi.org/10.1016/j.renene.2015.11.005.

[28] H. Ye, B. Yang, Y. Han & N. Chen, “State-Of-The-Art Solar Energy Forecasting Approaches: Critical Potentials and Challenges”, Front Energy Res 10 (2022) 875790. https://doi.org/10.3389/fenrg.2022.875790.

[29] H.T.C. Pedro & C.F.M. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs”, Solar Energy 86 (2012) 2017. https://doi.org/10.1016/j.solener.2012.04.004.

[30] M. Bouzerdoum, A. Mellit & A. Massi Pavan, “A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale gridconnected photovoltaic plant”, Solar Energy 98 (2013) 226. https://doi.org/10.1016/j.solener.2013.10.002.

[31] A. Di Piazza, M. C. Di Piazza & G. Vitale, “Solar and wind forecasting by NARX neural networks”, Renewable Energy and Environmental Sustainability 1 (2016) 39. https://doi.org/10.1051/rees/2016047.

[32] N. D. Kaushika, R. K. Tomar & S. C. Kaushik, “Artificial neural network model based on interrelationship of direct, diffuse and global solar radiations”, Solar Energy 103 (2014) 327. https://doi.org/10.1016/j.solener.2014.02.015.

[33] P. Tang, D. Chen & Y. Hou, “Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting”, Chaos Solitons Fractals 89 (2016) 243. https://doi.org/10.1016/j.chaos.2015.11.008.

[34] B. Yang, T. Zhu, P. Cao, Z. Guo, C. Zeng, D. Li, Y. Chen, H. Ye, R. Shao, H. Shu & Tao Yu, “Classification and summarization of solar irradiance and power forecasting methods: A thorough review”, CSEE Journal of Power and Energy Systems 9 (2021) 978. https://doi.org/10.17775/CSEEJPES.2020.04930.

[35] S. Huang, Q. Wu, W. Liao, G. Wu, X. Li & J. Wei, “Adaptive DroopBased Hierarchical Optimal Voltage Control Scheme for VSC-HVdc Connected Offshore Wind Farm”, IEEE Trans Industr Inform 17 (2021) 8165. https://doi.org/10.1109/TII.2021.3065375.

[36] M. Marzouglal, A. Souahlia, L. Bessissa, D. Mahi, A. Rabehi, Y.Z. Alharthi, A.K. Bojer, A. Flah, M.M. Alharthi, S. S. M. Ghoneim, “Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques”, Sci Rep 14 (2024) 25931. https://doi.org/10.1038/s41598-024-77112-3.

[37] T. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli & R. J. Hyndman, “Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond”, Int J Forecast 32 (2016) 896. https://doi.org/10.1016/j.ijforecast.2016.02.001.

[38] M. Russo, G. Leotta, P. M. Pugliatti & G. Gigliucci, “Genetic programming for photovoltaic plant output forecasting”, Solar Energy 105 (2014) 264. https://doi.org/10.1016/j.solener.2014.02.021.

[39] M. Rana, I. Koprinska & V. G. Agelidis, “Univariate and multivariate methods for very short-term solar photovoltaic power forecasting”, Energy Convers Manag 121 (2016) 380. https://doi.org/10.1016/j.enconman.2016.05.025.

[40] K. P. Lin & P. F. Pai, “Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression”, J Clean Prod 134 (2016) 456. https://doi.org/10.1016/j.jclepro.2015.08.099.

[41] F. Golestaneh, P. Pinson & H. B. Gooi, “Very short-term nonparametric probabilistic forecasting of renewable energy generation— with application to solar Energy”, IEEE Transactions on Power Systems 31 (2016) 3850. https://doi.org/10.1109/TPWRS.2015.2502423.

[42] R. J. Bessa, A. Trindade, C. S. P. Silva & V. Miranda, “Probabilistic solar power forecasting in smart grids using distributed information”, International Journal of Electrical Power & Energy Systems 72 (2015) 16. https://doi.org/10.1016/j.ijepes.2015.02.006.

[43] M. Hossain, S. Mekhilef, M. Danesh, L. Olatomiwa & S. Shamshirband,“ Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems”, J Clean Prod 167 (2017) 395. https://doi.org/10.1016/j.jclepro.2017.08.081.

[44] V. P. A. Lonij, A. E. Brooks, A. D. Cronin, M. Leuthold & K. Koch, “Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors”, Solar Energy 97 (2013) 58. https://doi.org/10.1016/j.solener.2013.08.002.

[45] A. G. R. Vaz, B. Elsinga, W.G.J.H.M. van Sark, “M.C. Brito, An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht”, the Netherlands, Renew Energy 85 (2016) 631. https://doi.org/10.1016/j.renene.2015.06.061.

[46] R. Dey & F. M. Salem, “Gate-variants of Gated Recurrent Unit (GRU) neural networks”, in IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA, 2017, pp. 1597–1600. https://doi.org/10.1109/MWSCAS.2017.8053243.

[47] S. Hochreiter & J. Schmidhuber, “Long Short-Term Memory”, Neural Comput 9 (1997) 1735. https://doi.org/10.1162/neco.1997.9.8.1735.

[48] F. A. Gers & J. Schmidhuber, “Recurrent nets that time and count”, Neural Computing 3 (2000) 189. https://doi.org/10.1109/IJCNN.2000.861302.

[49] H. Alizadegan, B. Rashidi Malki, A. Radmehr, H. Karimi, M. A. Ilani, “Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction”, Energy Exploration & Exploitation 43 (2025) 281. https://doi.org/10.1177/01445987241269496.

[50] K. Zhang, X. Huo & K. Shao, “Temperature time series prediction model based on time series decomposition and Bi-LSTM Network”, Mathematics 11 (2023) 2060. https://doi.org/10.3390/math11092060.

[51] B. Jang, M. Kim, G. Harerimana, S. Kang & J. W. Kim, “Bi-LSTM model to increase accuracy in text classification: combining word2vec cnn and attention mechanism”, Applied Sciences 10 (2020) 5841. https://doi.org/10.3390/app10175841.

[52] M. Guermoui, A. Rabehi, S. Benkaciali, D. Djafer, “Daily global solar radiation modelling using multi-layer perceptron neural networks in semiarid region”, Leonardo Electronic Journal of Practices and Technologies 28 (2016) 35. http://lejpt.academicdirect.org/A28/035_046.pdf.

[53] A. Rabehi, M. Guermoui, R. Khelifi & M. L. Mekhalfi, “Decomposing global solar radiation into its diffuse and direct normal radiation”, International Journal of Ambient Energy 41 (2020) 738. https://doi.org/10.1080/01430750.2018.1492445.

[54] A. Rabehi, M. Guermoui & D. Lalmi, “Hybrid models for global solar radiation prediction: a case study”, International Journal of Ambient Energy 41 (2020) 31. https://doi.org/10.1080/01430750.2018.1443498.

[55] N. El-Amarty, M. Marzouq, H. El Fadili, S. Dosse Bennani, A. Ruano, A. Rabehi, “A new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zones”, Energy Convers Manag 310 (2024) 118471. https://doi.org/10.1016/j.enconman.2024.118471.

[56] A. Bouchakour, L. Zarour, N. Bessous, M. Bechouat, A. Borni, L. Zaghba, A. Rabehi, A. Alwabli, M. El-Abd, S. S. M. Ghoneim, “MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance”, Sci Rep 14 (2024) 17891. https://doi.org/10.1038/s41598-024-68584-4.

[57] D. X. Zhou, “Theory of deep convolutional neural networks: Downsampling”, Neural Networks 124 (2020) 319. https://doi.org/10.1016/j.neunet.2020.01.018.

[58] A. Krizhevsky, I. Sutskever & G. E. Hinton, “ImageNet classification with deep convolutional neural networks”, Commun ACM 60 (2017) 84. https://doi.org/10.1145/3065386.

[59] W. Lu, J. Li, J. Wang & L. Qin, “A CNN-BiLSTM-AM method for stock price prediction”, Neural Comput Appl 33 (2021) 4741. https://doi.org/10.1007/s00521-020-05532-z.

[60] T. Arrif, A. Benchabane, M. Guermoui, A. Gama & H. Merarda, “Optical performance study of different shapes of solar cavity receivers used in central receiver system plant”, International Journal of Ambient Energy 42 (2021) 81. https://doi.org/10.1080/01430750.2018.1525584.

[61] D. Palaz, M. Magimai-Doss & R. Collobert, “End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition”, Speech Commun 108 (2019) 15. https://doi.org/10.1016/j.specom.2019.01.004.

[62] B. Ladjal, M. Nadour, M. Bechouat, N. Hadroug, M. Sedraoui, A. Rabehi, M. Guermoui & T. F. Agajie, “Hybrid deep learning CNNLSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria”, Sci Rep 15 (2025) 15404. https://doi.org/10.1038/s41598-025-94239-z.

[63] A. Teta, B. Korich, D. Bakria, N. Hadroug, A. Rabehi, M. Alsharef, M. Bajaj, I. Zaitsev & S. S. M. Ghoneim, “Fault detection and diagnosis of grid-connected photovoltaic systems using energy valley optimizer based lightweight CNN and wavelet transform”, Sci Rep 14 (2024) 18907. https://doi.org/10.1038/s41598-024-69890-7.

[64] A. Rabehi, B. Nail, H. Helal, A. Douara, A. Ziane, M. Amrani, B. Akkal & Z. Benamara, “Optimal estimation of Schottky diode parameters using a novel optimization algorithm: Equilibrium optimizer”, Superlattices Microstruct 146 (2020) 106665. https://doi.org/10.1016/j.spmi.2020.106665.

[65] A. Tasdelen & B. Sen, “A hybrid CNN-LSTM model for premiRNA classification”, Sci Rep 11 (2021) 14125. https://doi.org/10.1038/s41598-021-93656-0.

[66] O. Baitiche, F. Bendelala, A. Cheknane, A. Rabehi & E. Comini, “Numerical modeling of hybrid solar/thermal conversion efficiency enhanced by metamaterial light scattering for Ultrathin PbS QDs-STPV cell”, Crystals (Basel) 14 (2024) 668. https://doi.org/10.3390/cryst14070668.

[67] B. P. Babu & S. J. Narayanan, “One-vs-All Convolutional Neural Networks for synthetic aperture radar target recognition”, Cybernetics and Information Technologies 22 (2022) 179. https://doi.org/10.2478/cait-2022-0035.

[68] A. Ziane, A. Rabehi, A. Rouabhia, M. Amrani, A. Douara, R. Dabou, A. Necaibia, M. Mostefaoui & N. Sahouane, “Numerical Investigation of G–V Measurements of metal – A Nitride GaAs junction”, Revista Mexicana de F´?sica 70 (2024) 061604. https://doi.org/10.31349/RevMexFis.70.061604.

[69] H. Helal, Z. Benamara, M. Ben Arbia, A. Khettou, A. Rabehi, A. H. Kacha, M. Amrani, “A study of current-voltage and capacitance-voltage characteristics of Au/n-GaAs and Au/GaN/n-GaAs Schottky diodes in wide temperature range”, International Journal of Numerical Modelling: Electronic Networks Devices and Fields 33 (2020) e2714. https://doi.org/10.1002/jnm.2714.

[70] M. Guermoui, J. Boland & A. Rabehi, “On the use of BRL model for daily and hourly solar radiation components assessment in a semiarid climate”, The European Physical Journal Plus 135 (2020) 214. https://doi.org/10.1140/epjp/s13360-019-00085-0.

[71] A. Rabehi, A. Rabehi & M. Guermoui, “Evaluation of Different Models for Global Solar Radiation Components Assessment”, Applied Solar Energy 57 (2021) 81. https://doi.org/10.3103/S0003701X21010060.

[72] M. Guermoui, A. Rabehi, K. Gairaa & S. Benkaciali, “Support vector regression methodology for estimating global solar radiation in Algeria”, The European Physical Journal Plus 133 (2018) 22. https://doi.org/10.1140/epjp/i2018-11845-y.

Published

2025-08-01

How to Cite

A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2795. https://doi.org/10.46481/jnsps.2025.2795

Issue

Volume 7, Issue 3, August 2025

Section

Computer Science
Copyright & Licensing

Copyright (c) 2025 Mokhtar Ali, Abdelkerim Souahlia, Abdelhalim Rabehi, Mawloud Guermoui, Ali Teta, Imad Eddine Tibermacine, Abdelaziz Rabehi, Mohamed Benghanem

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2795. https://doi.org/10.46481/jnsps.2025.2795

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