In the coming years, the share of hybrid electric vehicles is expected to grow significantly in personal transportation. Vehicles that can be charged from the electrical grid, such as plug-in hybrids, could introduce problems for the distribution network, especially if the vehicle adoption is spatially concentrated and the charging happens unmanaged. Thus, where and when the hybrid vehicle adoption occurs is an important question for policy makers and planners.
Currently, promoted by the European Union directives such as INSPIRE and PSI, there is a trend of public sector data being harmonised and opened for free usage across the Europe. The vast amount of information in the various registers of the society has a huge and largely untapped potential for modelling societal and environmental phenomena. To discover correlations and relationships from such large databases, data mining methods can be useful.
In this paper, we utilise a data mining approach to identify relationships from public sector data between hybrid vehicle adoption and small area level socio-demography. Based on the discovered model, we assess how favourable each area type is for the adoption. The approach combines Self-Organizing Map based data compression, a feature selection using Genetic Algorithm and a linear regression model.
BassF.M., GordonK., FergusonT.L. and GithensM.L., Directv: Forecasting diffusion of a new technology prior to product launch, Interfaces31(3) (2001), S82-S93.
2.
CampbellA.R., RyleyT. and ThringR., Identifying the early adopters of alternative fuel vehicles: A case study of birmingham, united kingdom, Transportation Research Part A: Policy and Practice46(8) (2012), 1318-1327.
3.
CuiX., KimH.K., LiuC., KaoS.-C. and BhaduriB.L., Simulating the household plug-in hybrid electric vehicle distribution and its electric distribution network impacts, Transportation Research Part D: Transport and Environment17(7) (2012), 548-554.
4.
DiehrP., Small area statistics: large statistical problems, American Journal of Public Health74(4) (1984), 313-314.
5.
EisingJ.W., van OnnaT. and AlkemadeF., Towards smart grids: Identifying the risks that arise from the integration of energy and transport supply chains, Applied Energy123 (2014), 448-455.
6.
ElmoudiA., LehtonenM. and NordmanH., Effect of harmonics on transformers loss of life, in: Electrical Insulation, 2006, Conference Record of the 2006 IEEE International Symposium on (2006), 408-411.
7.
ErdemC., ŞentürkI. and ŞimşekT., Identifying the factors affecting the willingness to pay for fuel-efficient vehicles in turkey: A case of hybrids, Energy Policy38(6) (2010), 3038-3043.
8.
European Commission, The EU climate and energy package, 2009, Accessed 7 Nov 2012, Available from: http://ec. europa.eu/clima/policies/package/index_en.htm.
9.
European Commission, Reducing emissions from transport, 2011, Accessed 7 Nov 2012, Available from: http://ec. europa.eu/clima/policies/transport/index_en.htm.
10.
FarmerC., HinesP., DowdsJ. and BlumsackS., Modeling the impact of increasing phev loads on the distribution infrastructure, in: Proceedings of the 43nd Hawaii International Conference on System Sciences (HICSS-43) (2010), 1-10.
11.
FonsecaC.M. and FlemingP.J., Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization, in: Proceedings of the 5th International Conference on Genetic Algorithms, S. Forrest, ed., San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., 1993, pp. 416-423.
12.
FotheringhamA.S. and WongD.W.S., The modifiable areal unit problem in multivariate statistical analysis, Environment and Planning A23(7) (1991), 1025-1044.
13.
GehlkeC.E. and BiehlK., Certain effects of grouping upon the size of the correlation coefficient in census tract material, Journal of the American Statistical Association29(185) (1934), 169-170.
14.
HarrisC.B. and WebberM.E., An empirically-validated methodology to simulate electricity demand for electric vehicle charging, Applied Energy126 (2014), 172-181.
15.
HeffnerR.R., KuraniK.S. and TurrentineT.S., Symbolism in california's early market for hybrid electric vehicles, Transportation Research Part D: Transport and Environment12(6) (2007), 396-413.
16.
HerzogT., World greenhouse gas emissions in 2005, WRI Working Paper, 2009, Accessed 5 Jun 2013, Available from: http://www.wri.org/publication/world-greenhouse-gas-emissions-in-2005.
17.
KellyJ.C., MacDonaldJ.S. and KeoleianG.A., Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics, Applied Energy94 (2012), 395-405.
18.
KohonenT., Self-organizing Maps, volume 30 of Springer Series in Information Sciences, Springer, Berlin, 1995.
19.
KrupaJ.S., ChatterjeeS., EldridgeE., RizzoD.M. and EppsteinM.J., Evolutionary feature selection for classification: A plug-in hybrid vehicle adoption application, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, GECCO '12, New York, NY, USA, ACM, (2012), 1111-1118.
20.
LassilaJ., HaakanaJ., PartanenJ., KoivurantaK. and PeltonenS., Network effects of electric vehicles - case from nordic country, in: CIRED 2011 - The 21st International Conference and Exhibition on Electricity Distribution, Frankfurt CD-ROM, art. (2011), 773.
21.
LopesJ.A.P., SoaresF.J. and AlmeidaP.M.R., Identifying management procedures to deal with connection of electric vehicles in the grid, in: PowerTech, 2009 IEEE Bucharest (2009), 1-8.
22.
NamdeoA., TiwaryA. and DziurlaR., Spatial planning of public charging points using multi-dimensional analysis of early adopters of electric vehicles for a city region, Technological Forecasting and Social Change, 2013, 101016/j.techfore.2013.08.032.
23.
RautiainenA., RepoS., JärventaustaP., MutanenA., VuorilehtoK. and JalkanenK., Statistical charging load modeling of phevs in electricity distribution networks using national travel survey data, Smart Grid, IEEE Transactions on3(4) (2012). 1650-1659.
24.
RobinsonW.S., Ecological correlations and the behavior of individuals, American Sociological Review15(3) (1950), 351-357,
25.
SaarenpääJ., KolehmainenM. and NiskaH., Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption, Applied Energy107 (2013), 456-464.
26.
ShaoS., PipattanasompornM. and RahmanS., Challenges of phev penetration to the residential distribution network, in: Power & Energy Society General Meeting, 2009 PES '09 IEEE, (2009), 1-8.
27.
SrinivasN. and DebK., Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation2(3) (1994), 221-248,
28.
TranM., BanisterD., BishopJ.D. and McCullochM.D., Simulating early adoption of alternative fuel vehicles for sustainability, Technological Forecasting and Social Change80(5) (2013), 865-875.
29.
TurrentineT.S. and KuraniK.S., Car buyers and fuel economy? Energy Policy35(2) (2007), 1213-1223.
30.
VesantoJ., HimbergJ., AlhoniemiE. and ParhankangasJ., Som toolbox for matlab 5, Technical Report, Helsinki University of Technology, 2000.
31.
WillmottC.J., On the validation of models, Physical Geography2 (1981), 184-194.
