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An assessment of the renewable energy potential using a clustering based data mining method. Case study in Romania

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  • Grigoras, Gheorghe
  • Scarlatache, Florina

Abstract

This paper presents an assessment of the renewable energy potential in Romania using a clustering based data mining method. The available data on installed capacity, level voltage, type of renewable technology and geographical location the renewable energy potential for electricity generation was mapped into representative zones using K-Means clustering algorithm. For each zone, the potential was assessed on voltage level and renewable energy generation technologies (wind, solar, hydro, biogas, biomass, and cogeneration). The zones obtained can be a useful working tool for retrofitting substations, upgrading of transmission and distribution lines and also for redesigning them at different parameters with respect to the overload. This information may enable the creation of specific programs to improve planning and development of the electric networks in Romania.

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  • Grigoras, Gheorghe & Scarlatache, Florina, 2015. "An assessment of the renewable energy potential using a clustering based data mining method. Case study in Romania," Energy, Elsevier, vol. 81(C), pages 416-429.
    • Handle: RePEc:eee:energy:v:81:y:2015:i:c:p:416-429
    DOI: 10.1016/j.energy.2014.12.054
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    References listed on IDEAS

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    1. Lund, Henrik, 2007. "Renewable energy strategies for sustainable development," Energy, Elsevier, vol. 32(6), pages 912-919.
    2. Calvert, K. & Pearce, J.M. & Mabee, W.E., 2013. "Toward renewable energy geo-information infrastructures: Applications of GIScience and remote sensing that build institutional capacity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 416-429.
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    Cited by:

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    2. Izadyar, Nima & Ong, Hwai Chyuan & Chong, W.T. & Leong, K.Y., 2016. "Resource assessment of the renewable energy potential for a remote area: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 908-923.
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    5. Katarzyna Chudy-Laskowska & Tomasz Pisula & Mirosław Liana & László Vasa, 2020. "Taxonomic Analysis of the Diversity in the Level of Wind Energy Development in European Union Countries," Energies, MDPI, vol. 13(17), pages 1-21, August.
    6. Sun, M. & Teng, F. & Konstantelos, I. & Strbac, G., 2018. "An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources," Energy, Elsevier, vol. 145(C), pages 871-885.
    7. Elizaveta Gavrikova & Yegor Burda & Vladimir Gavrikov & Ruslan Sharafutdinov & Irina Volkova & Marina Rubleva & Daria Polosukhina, 2019. "Clean Energy Sources: Insights from Russia," Resources, MDPI, vol. 8(2), pages 1-25, May.
    8. Năstase, Gabriel & Şerban, Alexandru & Năstase, Alina Florentina & Dragomir, George & Brezeanu, Alin Ionuţ & Iordan, Nicolae Fani, 2017. "Hydropower development in Romania. A review from its beginnings to the present," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 297-312.
    9. Năstase, Gabriel & Șerban, Alexandru & Dragomir, George & Brezeanu, Alin Ionuț & Bucur, Irina, 2018. "Photovoltaic development in Romania. Reviewing what has been done," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 523-535.
    10. Chidean, Mihaela I. & Caamaño, Antonio J. & Ramiro-Bargueño, Julio & Casanova-Mateo, Carlos & Salcedo-Sanz, Sancho, 2018. "Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2684-2694.

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