Abstract
In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg–Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Masrur AAA (2014) Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs). J King Saud Univ – Eng S. doi:10.1016/j.jksues.2014.05.001
Ahmet K, Hakan FO, Yasin V, Gonca OK (2011) Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey. Expert Syst Appl 38:8756–8762
Algerian Ministry of Energy and Mines. Renewable energy and energy efficiency program. Available from: <http://www.mem-algeria.org>, [accessed on 2015]
Alshihri MM, Azmy AM, El-Bisy MS (2009) Neural networks for predicting compressive strength of structural light weight concrete. Constr Build Mater 23(6):2214–2219
Antonio GF, Emilio SO, Antonio JSL, Ernesto LB (2011) Estimating net radiation at surface using artificial neural networks: a new approach. Theor Appl Climatol 106:263–279. doi:10.1007/s00704-011-0488-7
Boudghene Stambouli A, Khiat Z, Flazi S, Kitamura Y (2012) A review on the renewable energy development in Algeria: current perspective, energy scenario and sustainability issues. Renew Sust Energy Rev 16:4445–4460
Boukelia TE, Mecibah MS (2013) Parabolic trough solar thermal power plant: potential, and projects development in Algeria. Renew Sust Energy Rev 21:288–297
Brown M, Harris C (1994) Neural fuzzy adaptive modeling and control. Prentice-Hall, Englewood Cliffs, NJ
Celik AN, Muneer T (2013) Neural network based method for conversion of solar radiation data. Energ Convers Manage 67:117–124
Gope D, Gope PC, Thakur A, Yadav A (2015) Application of artificial neural network for predicting crack growth direction in multiple cracks geometry. Appl Soft Comput J. doi:10.1016/j.asoc.2015.02.003
Dahmani K, Notton G, Dizene R, Paoli C, Voyant C, Nivet ML, Karouk K (2014) Estimation of 5-min time-step data of tilted solar global irradiation using ANN (artificial neural network) model. Energ 70:374–381
El Hamzaoui Y, Rodriguez JA, Hernandez JA, Salazar V (2015) Optimization of operating conditions for steam turbine using an artificial neural network inverse. Appl Therm Eng 75:648–657
Gazela M, Tambouratzis T (2002) Estimation of hourly average solar radiation on tilted surface via ANNs. Int J Neural Syst 12(1):1–13
Ghumman AR, Ghazaw YM, Sohail AR, Watanabe K (2011) Runoff forecasting by artificial neural network and conventional model. Alexandria Eng J 50(4):345–350
Guo YM, Liu YG, Zeng GM, Hu XJ, Xu WH, Liu YQ, Liu SM, Sun HS, Ye J, Huang HJ (2014) An integrated treatment of domestic wastewater using sequencing batch biofilm reactor combined with vertical flow constructed wetland and its artificial neural network simulation study. Ecol Eng 64:18–26
Hambli R, Chamekh A, Bel Hadj SH (2006) Real-time deformation of structure using finite element and neural networks in virtual reality applications. Finite Elem Anal Des 42(11):985–991
Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan, NY, p. 2
Hilmi BC, Hikmet KC (2007) Modelling public transport trips by radial basis function neural networks. Math Comput Model 45:480–489
Hornick K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Net 2:359–366
Huan HY, Huan HJ (2008) Application of improved BP neural in economic forecasts. Statistics and Information Forum 23:58–62
Kalogirou S (2000) Applications of artificial neural networks for energy systems. Appl Energy 67(1–2):17–35
Kalogirou S (2001) Artificial neural networks in renewable energy systems: a review. Renew Sust Energ Rev 5(4):373–401
Kalogirou S (2003) Artificial intelligence for the modelling and control of combustion processes: a review. Prog Energ Combust 29(6):515–566
Kumar Yadav A, Malik H, Chandel SS (2014) Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models. Renew Sust Energy Rev 31:509–519
Laidi M, Hanini S, Cheggaga N, Nadjemi O (2014) Predicting global solar radiation for north Algeria. Renew Energy and Power Qual J ISSN 2172-038 X, No. 12
Lolas S, Olatunbosun OA (2008) Prediction of vehicle reliability performance using artificial neural networks. Expert Syst Appl 34:2360–2369
Mehleri ED, Zervas PL, Sarimveis H, Palyvos JA, Markatos NC (2010) A new neural network model for evaluating the performance of various hourly slope irradiation models: implementation for the region of Athens. Renew Energy 35:1357–1362
Mellit A, Kalogirou SA, Shaari S, Salhi H, Hadj Arab A (2008) Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: application for sizing a stand-alone PV system. Renew Energy 33:1570–1590
Mendelsohn L (1993) Preprocessing data for neural networks. Technical Analysis of Stocks and Commodities 52-58
Mihalakakou G, Santamouris M, Asimakopoulos DN (2000) The total solar radiation time series simulation in Athens, using neural networks. Theor Appl Climatol 66:185–197
Mraoui A, Khelif M, Benyoucef B (2014) Optimum tilt angle of a photovoltaic system: case study of Algiers and Ghardaia. IEEE Xplore Digital Library
Neshat N, Mahlooji H, Kazemi A (2011) An enhanced neural network model for predictive control of granule quality characteristics. Sci Iranica 18(3):722–730
Notton G, Paoli C, Ivanova L, Vasileva S, Nivet ML (2013) Neural network approach to estimate 10-min solar global irradiation values on tilted planes. Renew Energ 50:576–584
Plumb AP, Rowe RC, York P, Brown M (2005) Optimisation of the predictive ability of artificial neural network (ANN) models: a comparison of three ANN programs and four classes of training algorithm. Eur J Pharm Sci 25:395
Rafiq MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79(17):1541–1552
Rezrazi A, Hanini S, Laidi M (2015) An optimisation methodology of artificial neural network models for predicting solar radiation: a case study. Theor Appl Climatol. doi:10.1007/s00704-015-1398-x
Si-Moussa C, Hanini S, Derriche R, Bouhedda M, Bouzidi A (2008) Prediction of high-pressure vapor liquid equilibrium of six binary systems, carbon dioxide with six esters, using an artificial neural network model. Braz J Chem Engineering 25(1):183–199
Sulafa HE (2014) Artificial neural networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan. Alexandria Eng J 53(3):655–662
Vinay C, Vinay A, Ravindra N (2015) Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks. Expert Syst Appl 42:885–893
Witek-Krowiak A, Chojnacka K, Podstawczyk D, Dawiec A, Pokomeda K (2014) Application of response surface methodology and artificial neural network methods in modeling and optimization of biosorption process. Bioresour Technol. doi:10.1016/j.biortech.2014.01.021
Yacef R, Mellit A, Belaid S, Sen Z (2014) New combined models for estimating daily global solar radiation from measured air temperature in semi-arid climates: application in Ghardaïa, Algeria. Energ Convers Manage 79:606–615
Yaiche MR, Bouhanik A, Bekkouche SMA, Malek A, Benouaz T (2014) Revised solar maps of Algeria based on sunshine duration. Energ Convers Manage 82:114–123
Acknowledgments
The authors would like to thank the Centre for Development of Renewable Energies (Bouzareah-Algeria) and its units like Ghardaia, Adrar and also the radiometric station of Tamanrasset for providing the help and data used in this study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Laidi, M., Hanini, S., Rezrazi, A. et al. Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria). Theor Appl Climatol 128, 439–451 (2017). https://doi.org/10.1007/s00704-015-1720-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-015-1720-7