Abstract
In order to estimate daily solar radiation, this paper proposes Elman (ENN) and Feed forward backpropagation (FNN) neural networks. The time series data from the location of Kenitra City, Morocco is used to train the created models. Fletcher-Powell Conjugate Gradient (CGF), Scaled Conjugate Gradient (SCG), Resilient Backpropagation (RB), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquardt (LM), Polak-RibiĀ“ere Conjugate Gradient (CGP), and One Step Secant (OSS) are also used with both, the ENN and FNN to identify the best and effective training function for each. The models with various training algorithms are tested by using evaluation metrics root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The study showed that the Elman Neural Network had an excellent daily solar radiation prediction for Kenitra city. Good results have been obtained with Levenberg-Marquardt algorithm.
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References
Singla, P., Duhan, M., Saroha, S.: Solar irradiance forecasting using elman neural network with different training algorithms. In: Proceedings of the International Conference on Sustainable Development in Technology for 4th Industrial Revolution, vol. 22, pp.137ā141 (2021)
Boussaada, Z., Curea, O., Remaci, A., Camblong, H., Mrabet Bellaaj, N.: A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation. Energies 11(3), 3 (2018). https://doi.org/10.3390/en11030620
Chen, S.X., Gooi, H.B., Wang, M.Q.: Solar radiation forecast based on fuzzy logic and neural networks. Renew. Energy 60, 195ā201 (2013). https://doi.org/10.1016/j.renene.2013.05.011
Mohandes, M., Rehman, S., Rahman, S.M.: Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl. Energy 88(11), 4024ā4032 (2011). https://doi.org/10.1016/j.apenergy.2011.04.015
Wysocki, A., ÅawryÅczuk, M.: Elman neural network for modeling and predictive control of delayed dynamic systems. Arch. Control Sci. 26(1) (2016). https://doi.org/10.1515/acsc-2016-0007
Huang, X., Zhang, C., Li, Q., Tai, Y., Gao, B., Shi, J.: A comparison of hour-ahead solar irradiance forecasting models based on LSTM network. Math. Probl. Eng. 2020 (2020). https://doi.org/10.1155/2020/4251517
Amellas, Y., El Bakkali, O., Djebil, A., Echchelh, A.: Short-term wind speed prediction based on MLP and NARX network models keywords: artificial neural network daily prediction multi-layer perceptron (MLP) NARX recurrent neural network (RNN). Indones. J. Electr. Eng. Comput. Sci. 18 (2020). https://doi.org/10.11591/ijeecs.v18.i1.pp150-157
Elmousaid, R., Adnani, Y., El Hamdaouy, A., Elgouri, R.: Daily solar radiation prediction using NARX and MLP-NNs networks: a case study of Kenitra City, Morocco. In: 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), pp. 1ā6 Dec 2022. https://doi.org/10.1109/ICECOCS55148.2022.9983450
Amellas, Y., Djebli, Ī, Echchelh, Ī: Levenberg-marquardt training function using on MLP, RNN and elman neural network to optimize hourly forecasting in Tetouan City (Northern Morocco). J. Eng. Sci. Technol. Rev. 13(1), 67ā71 (2020). https://doi.org/10.25103/jestr.131.09
Chaimae, D., Hamidane, H., Guerbaoui, M., Ed-Dahhak, A., Lachhab, A.: Identification of greenhouse temperature system using time series based on the NARX model. In: 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), pp. 1ā5 Dec 2022. https://doi.org/10.1109/ICECOCS55148.2022.9982952
Le, X.-H., Ho, H.V., Lee, G., Jung, S.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7), 1387 (2019)
Lourakis, M.I.A.: A Brief Description of the Levenberg-Marquardt Algorithm Implemened by levmar
Sari, Y.: Performance evaluation of the various training algorithms and network topologies in a neural-network-based inverse kinematics solution for robots. Int. J. Adv. Robot. Syst. 11(4), 64 (2014). https://doi.org/10.5772/58562
Cetisli, B., Barkana, A.: Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Comput. 14, 365ā378 (2009). https://doi.org/10.1007/s00500-009-0410-8
Zakaria, Z., Isa, N.A.M., Suandi, S.A.: A Study on Neural Network Training Algorithm for Multiface Detection in Static Images. Int. J. Comput. Inf. Eng. 4(2), 345ā348 (2010)
Anushka, P., Md, A.H., Upaka, R.: Comparison of different artificial neural network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia. MAUSAM 71(2), 2 (2020). https://doi.org/10.54302/mausam.v71i2.22
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Elmousaid, R., Adnani, Y., Hamdaouy, A.E., Elgouri, R. (2023). Elman and Feed-Forward Neural Networks with Different Training Algorithms for Solar Radiation Forecasting: A Comparison with a Case Study. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 668. Springer, Cham. https://doi.org/10.1007/978-3-031-29857-8_1
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