Commenced in January 2007
Paper Count: 30127
Electricity Generation from Renewables and Targets: An Application of Multivariate Statistical Techniques
Abstract:Renewable energy is referred to as "clean energy" and common popular support for the use of renewable energy (RE) is to provide electricity with zero carbon dioxide emissions. This study provides useful insight into the European Union (EU) RE, especially, into electricity generation obtained from renewables, and their targets. The objective of this study is to identify groups of European countries, using multivariate statistical analysis and selected indicators. The hierarchical clustering method is used to decide the number of clusters for EU countries. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method and squared Euclidean distances. Hierarchical cluster analysis identified eight distinct clusters of European countries. Then, non-hierarchical clustering (k-means) method was applied. Discriminant analysis was used to determine the validity of the results with data normalized by Z score transformation. To explore the relationship between the selected indicators, correlation coefficients were computed. The results of the study reveal the current situation of RE in European Union Member States.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125947Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 705
 SE4ALL. “Tracking Progress.” http://www.se4all. Org /tracking–progress/, viewed 10 April 2015.
 European Wind Energy Association (EWEA). Wind in Power: 2014.
 Renewables 2015 Global Status Report. “Energy Efficiency: Renewable Energy’s Twin Pillar”. Industry. pp.118. 2015.
 IEA. Medium-Term Renewable Energy Market Report 2014. op. cit. note 9. 2014.
 Renewables 2015 Global Status Report. “Global Overview”. pp.34.
 IEA. World Energy Outlook 2014. Op. Cit. Note 9. p. 207. 2014.
 Figure of 8% from IEA. Medium-Term Renewable Energy Market Report 2014. op. cit. note 9. Werner Weiss. “How to Boost Renewables Heating and Cooling” conference presentation. REN21 Renewables Academy. Bonn. Germany. http://www.ren21.net/ REN21Activities/ Renewables Academy/BoostingRenewables.aspx.. 10 November 2015
 Renewables 2015 Global Status Report. “Global Overview”. Power Sector. pp.27. 30. 2015.
 China share based on data and references provided elsewhere in this section; 280 GW of hydropower from China National Energy Board. op. cit. note 41.
 Bp Global Report. http://www.bp.com/en/global/corporate/energy- economics/statistical-review-of-world-energy/renewable-energy/ renewables-in-this-review.html.viewed 04 April 2016.
 IEA 2014; BP 2015pbl.
 Y. Kuang et al. “A review of renewable energy utilization in islands”. Journal of Renewable and Sustainable Energy Reviews Vol. 59. pp. 504-513. 2016.
 E. Taibi et al. “A framework for technology cooperation to accelerate the deployment of renewable energy in Pacific Island Countries”. Journal of Energy Policy. In Press. 2016.
 C. Betzold. “Fueling the Pacific: Aid for renewable energy across Pacific Island countries”. Journal of Renewable and Sustainable Energy Reviews Vol. 58. pp. 311-318. 2016.
 P. K. Wesseh Jr. and B. Lin. “Can African countries efficiently build their economies on renewable energy?” Journal of Renewable and Sustainable Energy Reviews Vol. 54. pp. 161-173. 2016.
 Z. Abdmouleh et al. “Recommendations on renewable energy policies for the GCC countries”. Journal of Renewable and Sustainable Energy Reviews Vol. 50. pp. 1181-1191. 2015.
 K. Horiet al. “Development and application of the renewable energy regional optimization utility tool for environmental sustainability: Reroutes”. Journal of Renewable Energy Vol. 93. pp. 548-561. 2016.
 L. Sokka et al. “Environmental impacts of the national renewable energy targets – A case study from Finland”. Journal of Renewable and Sustainable Energy Reviews Vol. 59. pp. 1599-1610. 2016.
 M. A. Destek. “Renewable energy consumption and economic growth in newly industrialized countries: Evidence from asymmetric causality test”. Journal of Renewable Energy Vol. 95. pp. 478-484. 2016.
 K. Saidi and M. B. Mbarek. “Nuclear energy, renewable energy. CO2 emissions. and economic growth for nine developed countries: Evidence from panel Granger causality tests”. Journal of Progress in Nuclear Energy Vol. 88. pp. 364-374. 2016.
 E. Dogan and F. Seker. “The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries”. Journal of Renewable and Sustainable Energy Reviews Vol. 60. pp. 1074-1085. 2016.
 Y. Chang et al. “Renewable energy policies in promoting financing and investment among the East Asia Summit countries: Quantitative assessment and policy implications”. Journal of Energy Policy. In Press. 2016.
 S. F. Naseri et al. “Study of mediated consumption effect of renewable energy on economic growth of OECD countries”. Journal of Procedia Economics and Finance Vol. 36. pp. 502-509. 2016.
 M. Bhattacharya et al. “The effect of renewable energy consumption on economic growth: Evidence from top 38 countries”. Journal of Applied Energy Vol.162. pp. 733-741. 2016.
 Renewables 2015 Global Status Report. “Table R13, share of electricity generation from renewables, existing in 2013 and targets”, pp.140-141. 2015.
 United Nations Statistics Division. http://mdgs.un.org/unsd/mdg/ Series Detail.aspx?srid=75, viewed 04 April 2016.
 Europe Union. The Economy. http://europa.eu/about-eu/facts-figures /economy/index_en.htm, viewed 04 April 2016.
 Johnson RA. Wichern DW. “Part IV: Classification and grouping techniques, applied multivariate statistical analysis”. 1st edition. New Jersey. Prentice-Hall. Inc. pp 459-578. 1982.
 Jain AK. Duin PW. Mao J. “Statistical pattern recognition a review”. IEEE Transactions on Pattern Analysis and Machine Intelligence. pp 22:4–37. 2000.
 Jain AK. Murty MN. Flynn PJ. “Data clustering a review”. ACM Computing Surveys. 31. pp 264–323. 1999.
 Forgy EW. Cluster analysis of multivariate data efficiency. interpretability of classifications. Biometrics. 21. pp 768-69. 1965.
 Martin FL. German MJ. Wit E. Fearn T. Ragavan N. Pollock HM. “Identifying variables responsible for clustering in discriminant analysis of data from infrared micro spectroscopy of a biological sample”. Journal of Computational Biology. 14 pp 176-84. 2007.