Abstract
Solar radiation estimation is the most integral part of design and performance of solar energy applications. Our paper aims to develop an artificial neural networks-based model for predicting the daily global solar radiation in three cities (Bechar, Naâma and Tindouf) in the south-west region of Algeria. Models’ inputs are: average temperature, wind speed, relative humidity, atmospheric pressure, extraterrestrial solar irradiation, sunshine duration. Three ANN multilayer architectures connection are used with the Levenberg-Marquardt algorithm for training. Efficiency of models was assessed using statistical tests including, correlation coefficient (R), root mean squared error (RMSE), mean bias error (MBE) and mean absolute percentage error (MAPE).The results during five years showed that, the Cascade-forward Neural Network (CFNN) and Feed-forward neural network (FFNN) models gives much better forecast of daily global solar radiation in the three Saharan cities. The models developed can be used for design and sizing solar energy systems, where radiation measuring stations are scarce in Algeria.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, J., Zhao, L., Shuai, D., Weicong, X., Zhang, Y.: A critical review of the models used to estimate solar radiation. Renew. Sustain. Energ. Rev. 70, 314–329 (2017)
Bouchouicha, K., Bailek, N., Bellaoui, M., Oulimar, B.: Comparison of artificial intelligence and empirical models for energy production estimation of 20 MWp solar photovoltaic plant at the saharan medium of Algeria, Int. J. Energ. Sect. Manag. 195–203, Springer, Cham, (2020)
Benatiallah, D., Benatiallah, A., Bouchouicha, K., Nasri, B.: Estimation of clear sky global solar radiation in Algeria. AIMS Energ. 7, 710–727 (2019)
Yadav, A.K., Chandel, S.S.: Solar radiation prediction using artificial neural network techniques: a review. Renew. Sustain Energ. Rev. 33, 772–781 (2014)
Mellit, A., Benghanem, M., Bendekhis., M.: Artificial neural network model for prediction solar radiation data: application for sizing standalone photovoltaic power system. In: Power Engineering Society, General Meeting, USA, pp. 2187–2191. IEEE (2005)
Qazi, A., Fayaz, H., Wadi, A., et al.: The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J. Clean. Prod. 104, 1–12 (2015)
Yadav, A.K., Chandel, S.S.: Solar radiation prediction using artificial neural network techniques: a review. Renew. Sustain. Energ. Rev. 33, 72–81 (2014)
Kaushika, N.D., Tomar, R., Kaushik, S.C.: Artificial neural network model based on interrelationship of direct, diffuse and global solar radiations. Solar Energ. 103, 327–342 (2014)
Muammer, O., Bilgili, M., Sahin, B.: Estimation of global solar radiation using ANN over Turkey. Expert Syst. Appl. 39, 5043–5051 (2012)
Kumar, R., Aggarwal, R.K., Sharma, J.D.: Comparison of regression and artificial neural network models for estimation of global solar radiations. Renew. Sustain. Energ. Rev. 52, 1294–1299 (2015)
Benatiallah, D., Benatiallah, A., Bouchouicha, K., Nasri, B.: Prédiction du rayonnement solaire horaire en utilisant les réseaux de neurone artificiel. Algerian J. Environ. Sci. Technol. 6, 1236–1245 (2020)
Benatiallah, D., Benatiallah, A., Bouchouicha, K., Hamouda, M., Nasri, B.: An empirical model for estimating solar radiation in the Algerian Sahara. Am. Inst. Phys. 7, 710–727 (2018)
Benatiallah, D., Bouchouicha, K., Benatiallah, A., Harrouz, A., Nasri, B.: Forecasting of solar radiation using an empirical model. Algerian J. Renew. Energ. Sustain. Develop. 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. Syst. Model. Simul. 1, 15–19 (2016)
SODA data. www.soda-pro.com/web-services#meteodata
Vassilis, Z., Antonopoulos, D., Papamichail, et al.: Solar radiation estimation methods using ANN and empirical models. Comput. Electron. Agricul. 160, 160–167 (2019)
Raza, M.Q., Mithulananthan, N., Summerfield, A.: Solar output power forecast using an ensemble framework with neural predictors and Bayesian adaptive combination. Sol Energ. 166, 26–41 (2018)
Yadav, A.K., Chandel, S.S.: Solar radiation prediction using artificial neural network techniques: a review. Renew. Sustain. Energ. Rev. 33, 772–781 (2014)
Jain, S.K., Nayak, P.C., Sudheer, K.P.: Models for estimating evapotranspiration using artificial neural networks and their physical interpretation. Hydrol. Process. 22, 2225–2234 (2008)
Stone, R.J.: Improved statistical procedure for the evaluation of solar radiation estimation models. Solar Energ. 89, 51–91 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
.
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Benatiallah, D., Bouchouicha, K., Benatiallah, A., Harouz, A., Nasri, B. (2021). Artificial Neural Network Based Solar Radiation Estimation of Algeria Southwest Cities. In: Hatti, M. (eds) Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-63846-7_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-63846-7_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63845-0
Online ISBN: 978-3-030-63846-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)