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Artificial neural network analysis of Moroccan solar potential

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  • Ouammi, Ahmed
  • Zejli, Driss
  • Dagdougui, Hanane
  • Benchrifa, Rachid

Abstract

An artificial neural network (ANN) model is used to forecast the annual and monthly solar irradiation in Morocco. Solar irradiation data are taken from the new Satellite Application Facility on Climate Monitoring (CM-SAF)-PVGIS database. The database represents a total of 12 years of data from 1998 to 2010. In this paper, the data are inferred using an ANN algorithm to establish a forward/reverse correspondence between the longitude, latitude, elevation and solar irradiation. Specifically, for the ANN model, a three-layered, back-propagation standard ANN classifier is considered consisting of three layers: input, hidden and output layer. The learning set consists of the normalised longitude, latitude, elevation and the normalised mean annual and monthly solar irradiation of 41 Moroccan sites. The testing set consists of patterns just represented by the input component, while the output component is left unknown and its value results from the ANN algorithm for that specific input. The results are given in the form of the annual and monthly maps. They indicate that the method could be used by researchers or engineers to provide helpful information for decision makers in terms of sites selection, design and planning of new solar plants.

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  • Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    • Handle: RePEc:eee:rensus:v:16:y:2012:i:7:p:4876-4889
    DOI: 10.1016/j.rser.2012.03.071
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    1. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    2. Fritzsche, Kerstin & Zejli, Driss & Tänzler, Dennis, 2011. "The relevance of global energy governance for Arab countries: The case of Morocco," Energy Policy, Elsevier, vol. 39(8), pages 4497-4506, August.
    3. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
    4. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    5. Ouammi, Ahmed & Sacile, Roberto & Zejli, Driss & Mimet, Abdelaziz & Benchrifa, Rachid, 2010. "Sustainability of a wind power plant: Application to different Moroccan sites," Energy, Elsevier, vol. 35(10), pages 4226-4236.
    6. Rosiek, S. & Batlles, F.J., 2010. "Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network," Renewable Energy, Elsevier, vol. 35(12), pages 2894-2901.
    7. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
    8. Soares, Jacyra & Oliveira, Amauri P. & Boznar, Marija Zlata & Mlakar, Primoz & Escobedo, João F. & Machado, Antonio J., 2004. "Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique," Applied Energy, Elsevier, vol. 79(2), pages 201-214, October.
    9. Miguel, A.F. & Silva, A., 2010. "Solar irradiation in diffusely enclosures with partitions," Applied Energy, Elsevier, vol. 87(3), pages 836-842, March.
    10. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: A neural kriging application," Renewable Energy, Elsevier, vol. 33(6), pages 1251-1266.
    11. Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
    12. Almonacid, F. & Rus, C. & Pérez, P.J. & Hontoria, L., 2009. "Estimation of the energy of a PV generator using artificial neural network," Renewable Energy, Elsevier, vol. 34(12), pages 2743-2750.
    13. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    14. Riahy, G.H. & Abedi, M., 2008. "Short term wind speed forecasting for wind turbine applications using linear prediction method," Renewable Energy, Elsevier, vol. 33(1), pages 35-41.
    15. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
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    16. El Ydrissi, Massaab & Ghennioui, Hicham & Bennouna, El Ghali & Farid, Abdi, 2020. "Techno-economic study of the impact of mirror slope errors on the overall optical and thermal efficiencies- case study: Solar parabolic trough concentrator evaluation under semi-arid climate," Renewable Energy, Elsevier, vol. 161(C), pages 293-308.
    17. Mahdavi, Meisam & Jurado, Francisco & Ramos, Ricardo Alan Verdú & Awaafo, Augustine, 2023. "Hybrid biomass, solar and wind electricity generation in rural areas of Fez-Meknes region in Morocco considering water consumption of animals and anaerobic digester," Applied Energy, Elsevier, vol. 343(C).
    18. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    19. Anamika, & Peesapati, Rajagopal & Kumar, Niranjan, 2016. "Estimation of GSR to ascertain solar electricity cost in context of deregulated electricity markets," Renewable Energy, Elsevier, vol. 87(P1), pages 353-363.
    20. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Salazar, Germán Ariel & Zhu, Zhongmin & Gong, Wei, 2016. "Solar radiation prediction using different techniques: model evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 384-397.
    21. Kousksou, T. & Allouhi, A. & Belattar, M. & Jamil, A. & El Rhafiki, T. & Zeraouli, Y., 2015. "Morocco's strategy for energy security and low-carbon growth," Energy, Elsevier, vol. 84(C), pages 98-105.
    22. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.

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