New Model of Photovoltaic System Adapted By a Digital Mppt Control and Radiation Predictions Using Deep Learning in Morocco Agricultural Sector
By:
References
- M. Azerouia, A. El Makrini, H. El Moussaoui, and H. El Markhi. “Renewable Energy Potential and Available Capacity for Wind and Solar Power in Morocco Towards 2030,” Journal of Engineering Science and Technology Review, 2018.
- Y. A. Al-Sbou, and K. M. Alawasa. “Nonlinear autoregressive recurrent neural network model for solar radiation prediction,” Int. J. Appl. Eng. Res., vol. 12, 2017, 4518–4527.
- E. F. Alsina, M. Bortolini, M. Gamberi, and A. Regattieri. “Artificial neural network optimisation for monthly average daily global solar radiation prediction,” Energy Convers. Manag. 120, 2016, 320–329.
- A. Angstrom. “Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation,” Q. J. R. Meteorol. Soc., 50, 1924, 121–126.
- J. Annandale, N. Jovanovic, N. Benade, and R. Allen. “Software for missing data error analysis of Penman-Monteith reference evapotranspiration,” Irrig. Sci., 21, 2002, 57–67.
- V. Z. Antonopoulos, D. M. Papamichail, V. G. Aschonitis, and A. V. Antonopoulos. “Solar radiation estimation methods using ANN and empirical models,” Comput. Electron. Agric., vol. 160, 2019, 160–167.
- K. Basaran, A. Özçift, and D. Kılınç. “A new approach for prediction of solar radiation with using ensemble learning algorithm,” Arab. J. Sci. Eng., vol. 44, 2019, 7159–7171.
- M. Behrang, E. Assareh, A. Ghanbarzadeh, A. Noghrehabadi. “The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data,” Sol. Energ., vol. 84, 2010, 1468–1480.
- M. Benghanem, A. Mellit, S. Alamri. “ANN-based modelling and estimation of daily global solar radiation data: a case study,” Energy Convers. Manag., vol. 50, 2009, 1644–1655.
- F. Besharat, A. A. Dehghan, A. R. Faghih. “Empirical models for estimating global solar radiation: a review and case study,” Renew. Sust. Energ. Rev., vol. 21, 2013, 798–821.
- Z. Boussaada, O. Curea, A. Remaci, H. Camblong, and N. Mrabet Bellaaj. “A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation,” Energies, vol. 11, 2018, 620.
- L. Breiman. “Random forests,” Mach. Learn., vol. 45, 2001, 5–32.
- L. Breiman. “Bagging predictors,” Mach. Learn., vol. 24, 1996, 123–140.
- M. V. C. Calça, M. R. Raniero, D. M. Z. Fernando, S. A. Rodrigues, and A. Dal Pai. “Outliers detection in a quality control procedure for measurements of solar radiation,” IEEE Lat. Am. Trans., vol. 17, 2019, 1815–1822.
- J. Chen, G. Li. “Estimation of monthly average daily solar radiation from measured meteorological data in Yangtze River Basin in China,” Int. J. Climatol., 33, 2013, 487–498.
- J. L. Chen, L. He, H. Yang, M. Ma, Q. Chen, S. J. Wu, and Z. Xiao. “Empirical models for estimating monthly global solar radiation: a most comprehensive review and comparative case study in China,” Renew. Sust. Energ. Rev., vol. 108, 2019, 91–111.
- R. Chen, E. Kang, S. Lu, J. Yang, X. Ji, Z. Zhang, and J. Zhang. “New methods to estimate global radiation based on meteorological data in China,” Energ. Convers. Manag., vol. 47, 2006, 2991–2998.
- M. Despotovic, V. Nedic, D. Despotovic, S. Cvetanovic. “Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation,” Renew. Sust. Energ. Rev., vol. 56, 2016, 246–260.
- D. Elizondo, G. Hoogenboom, and R. McClendon. “Development of a neural network model to predict daily solar radiation,” Agric. For. Meteorol., vol. 71, 1994, 115–132.
- P. N. Jyothy, and M. R. Sindhu. “An Artificial Neural Network based MPPT Algorithm For Solar PV System,” 2018 4th International Conference on Electrical Energy Systems (ICEES), Chennai, India, 2018, pp. 375–380, doi: 10.1109/ICEES.2018. 8443277.
- J. Fan, L. Wu, F. Zhang, H. Cai, W. Zeng, X. Wang, and H. Zou. “Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: a review and case study in China,” Renew. Sust. Energ. Rev., vol. 100, 2019, 186–212.
- Y. Feng, N. Cui, Q. Zhang, L. Zhao, and D. Gong. “Comparison of artificial intelligence and empirical models for estimation of daily diffuse solar radiation in North China Plain,” Int. J. Hydrog. Energ., vol. 42, 2017, 14418–14428.
- B. Benamrou, M. Ouardouz, I. Allaouzi, and M. B. Ahmed. “A Proposed Model to Forecast Hourly Global Solar Irradiation Based on Satellite Derived Data, Deep Learning and Machine Learning Approaches” Journal of Ecological Engineering, vol. 21, no. 4, 2020, 26–38.
- W. Bendali, Y. Mourad, S. Ikram, and B. Mohammed. “Deep Learning Using Genetic Algorithm Optimization for Short Term Solar Irradiance Forecasting,” Conference: International Conference on Intelligent Computing in Data Sciences (ICDS) At: Fes, December 2020.
- Y. Stitou. “Case Study: Masen NOOR Ouarzazate Solar Complex,” The Center for Mediterranean Integration, 2017.
- W. Bendali, Y. Mourad, S. Ikram, and B. Mohammed. “Deep Learning Using Genetic Algorithm Optimization for Short Term Solar Irradiance Forecasting,” Conference: International Conference on Intelligent Computing in Data Sciences (ICDS) At: Fes December 2020.
- A. Aghmadi, S. El Hani, H. Mediouni, N. Naseri, and F. El Issaoui. “Hybrid Solar Forecasting Method Based on Empirical Mode Decomposition and Back Propagation Neural Network,” E3S Web of Conferences, 2020 2nd International Conference on Power, Energy and Electrical Engineering (PEEE 2020), vol. 231, no. 02001, 2021.
- Y. Stitou. “Case Study: Masen NOOR Ouarzazate Solar Complex,” The Center for Mediterranean Integration, 2017.
- M. Azeroual, A. El Makrini, H. El Moussaoui, and H. El Markhi. “Renewable Energy Potential and Available Capacity for Wind and Solar Power in Morocco Towards 2030,” Journal of Engineering Science and Technology Review, vol. 11, no. 1, 2018, 189–198.
- Z. Bounoua, L. O. Chahidi, and A. Mechaqrane. “Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations,” Sustainable Materials and Technologies, vol. 28, 2021, e00261.
- O. El Alani, H. Ghennioui, and A. Ghennioui. “Short term solar irradiance forecasting using artificial neural network for a semi-arid climate in Morocco,” 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), 2019.
- O. Nait Mensour, S. Bouaddi, B. Abnay, B. Hlimi, and A. Ihlal. “Mapping and estimation of monthly global solar irradiation in different zones in Souss-Massa area, Morocco, using artificial neural networks,” International Journal of Photoenergy, vol. 2017, no. 8547437, 2017.
- M. A. Jallal, A. El Yassini, S. Chabaa, A. Zeroual, and S. Ibnyaich. “AI data driven approachbased endogenous inputs for global solar radiation forecasting,” Ingénierie des systems d’Information, vol. 25, no. 1, 2020, 27–34. doi: 10.18280/isi.250104.
- S. Alouini, and S. Calcagno. “Les réseaux de neurones récurrents: des RNN simples aux LSTM,” OCTO BLOG, 2019.
- H. Ettaybi, and K. El Himdi. “Artificial neural networks for forecasting the 24 hours ahead of global solar irradiance,” AIP Conference Proceedings, vol. 2056, no. 1, 2018, 020010. doi: 10.1063/1.5084983.
Language: English
Page range: 74 - 84
Submitted on: May 12, 2022
Accepted on: Apr 26, 2023
Published on: Jan 26, 2024
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2024 Amal Zouhri, Mostafa El Mallahi, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.