Abstract
Estimating solar irradiance is an essential step in the design of solar systems and the performance evaluation of their various applications. This work has the purpose of developing a model based on an Artificial Neural Network (ANN) to anticipate the global solar irradiance on a daily basis in the city of Bechar. The models were given seven input data. We developed four models using different training algorithms. Correlation coefficient (R) and mean absolute percentage error (MAPE) were used to assess these models' efficiency. The results over 6 years demonstrated that Model1, provides significantly better forecasts with (R = 0.9198 and MAPE = 7.57). Therefore, in the Multi-Layer Feed Forward Neural Network (MLF), using the Levenberg-Marquardt back-propagation training algorithm provides the best accuracy for estimating daily solar radiation and may be considered one of the fastest and most accurate algorithms. This model is useful for sizing and designing solar systems in Algeria.
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References
Zhang, J., Zhao, L., Deng, S., Xu, W., Zhang, Y.: A critical review of the models used to estimate solar radiation. Renew. Sustain. Energy Rev. 70, 314–329 (2017)
Zendehboudi, A., Baseer, M.A., Saidur, R.: Application of support vector machine models for forecasting solar and wind energy resources: A review. J. Clean. Prod. 199, 272–285 (2018)
Despotovic, M., Nedic, V., Despotovic, D., Cvetanovic, S.: Review and statistical analysis of different global solar radiation sunshine models. Renew. Sustain. Energy Rev. 52, 1869–1880 (2015)
Zhou, Y., Liu, Y., Wang, D., Liu, X., Wang, Y.: A review on global solar radiation prediction with machine learning models in a comprehensive perspective. Energy Convers. Manage. 235, 113960 (2021)
Baser, F., Demirhan, H.: A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation. Energy (2017). https://doi.org/10.1016/j.energy.2017.02.008
Kabouche, N., Chellali, F., Recioui, A.: A review on solar radiation assessment and forecasting in algeria (part 2: solar radiation forecasting). Algerian Journal Of Signals And Systems (AJSS) 6(3) (September-2021). ISSN: 2543-3792-EISSN: 2676-1548
Benkaciali, S., Haddadi, M., Khellaf, A., Gairaa, K., Guermoui, M.: 2016, Evaluation of the global solar irradiation from the artificial neural network technique. Revue des Energies Renouvelables 19(4), 617–631 (2016)
Benatiallah, D., Benatiallah, A., Bouchouicha, K., Nasri, B.: Prediction du rayonnement solaire horaire en utilisant les reseaux de neurone artificiel. Algerian J. Env. Sc. Technology 6(1), 1236–1245 (2020)
Antonopoulos, V.Z., Papamichail, D.M., Aschonitis, V.G., Antonopoulos, A.V.: Solar radiation estimation methods using ANN and empirical models. Comput. Electron. Agric. 160, 160–167 (2019). https://doi.org/10.1016/j.compag.2019.03.022
Benatiallah, D., Benatiallah, A., Bouchouicha, K., Hamouda, M., Nasri, B.: An empirical model for estimating solar radiation in the Algerian Sahara. American Institute of Physics 7, 710–727 (2018)
Benatiallah, D., Bouchouicha, K., Benatiallah, A., Harrouz, A., Nasri, B.: Forecasting of Solar Radiation using an Empirical Model. Algerian Journal of Renewable Energy and Sustainable Development 1, 212–219 (2019)
Benatiallah, D., Benatiallah, A., Harouz, A., Bouchouicha, K.: Development and modeling of a geographic information system solar flux in adrar, Algeria. Int. J. Sys. Model. Simul. 1, 15–19 (2016)
SODA data: Available at: www.soda-pro.com/web-services#meteodata
Haykin, S., Lippmann, R.: Neural networks, A Comprehensive Foundation. Int. J. Neural Syst. 5, 363–364 (1994)
Yadav, A.K., Chandel, S.S.: Solar radiation prediction using artificial neural network techniques: a review. Renew. Sustain. Energy Rev. 33, 772–781 (2014). https://doi.org/10.1016/j.rser.2013.08.055
Ata, R.: Artificial neural networks applications in wind energy systems: a review. Renew. Sustain. Energy Rev. 49, 534–562 (2015). https://doi.org/10.1016/j.rser.2015.04.166
Esmaeelzadeh, S.R., Adib, A., Alahdin, S.: Long-term streamflow forecasts by adaptive neuro-fuzzy inference system using satellite images and K-fold crossvalidation (case study: Dez, Iran). KSCE J. Civ. Eng. 1–9 (2014)
Rezrazi, A., Hanini, S., Laidi, M.: An optimisation methodology of artificial neural network models for predicting solar radiation: a case study. Theoret. Appl. Climatol. 123(3–4), 769–783 (2015). https://doi.org/10.1007/s00704-015-1398-x
Stone, R.J.: Improved statistical procedure for the evaluation of solar radiation estimation models. Sol. Energy 89, 51–91 (1993)
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Djeldjli, H., Benatiallah, D., Bouchouicha, K., Benatiallah, A. (2023). Solar Radiation Forecasting Based on Artificial Neural Network: A Case Study of Bechar City, Southwest Algeria. In: Hatti, M. (eds) Advanced Computational Techniques for Renewable Energy Systems. IC-AIRES 2022. Lecture Notes in Networks and Systems, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-031-21216-1_1
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DOI: https://doi.org/10.1007/978-3-031-21216-1_1
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