Abstract
Electric vehicles (EVs) can play a central role in today’s efforts to reduce CO\(_2\) emission and slow down the climate change. Two of the most important reasons against purchase or use of an EV are its short range and long charging times. In the project “E-WALD—Elektromobilität Bayerischer Wald”, we develop mathematical models to predict the range of EVs by estimating the electrical power consumption (EPC) along possible routes. Based on the EPC forecasts the range is calculated and visualized by a range polygon on a navigation map. The models are based on data that are constantly collected by cars within a commercial car fleet. The dataset is modelled with three methods: a linear model, an additive model and a fully nonparametric model. To fit the linear model, ordinary least squares (OLS) regression as well as linear median regression are applied. The other models are fitted by modern machine learning algorithms: the additive model is fitted by boosting algorithm and the fully nonparametric model is fitted by support vector regression (SVR). The models are compared by mean absolute error (MAE). Our research findings show that data preparation is more influential than the chosen model.
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References
Bühlmann, P., Hothorn, T.: Boosting algorithms: regularization, prediction and model fitting (with discussion). Stat. Sci. 22, 477–505 (2007). doi:10.1214/07-STS242
Bühlmann, P., Yu, B.: Boosting with the \(l_2\)-loss: regression and classification. J. Am. Stat. Assoc. 98(462), 324–338 (2003). doi:10.1198/016214503000125
Freund, Y., Schapire, R.E. (eds.): Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning Theory, Morgan Kaufmann Publishers Inc., San Francisco (1996)
Freund, Y., Schapire, R.E.: A decision—theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997). doi:10.1006/jcss.1997.1504
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001). doi:10.1214/aos/1013203451
Friedman, J.H., Stuetzle, W.: Projection pursuit regression. J. Am. Stat. Assoc. 76(376), 817–823 (1981). doi:10.1080/01621459.1981.10477729
Hastie, T.J., Tibshirani, R.J., Friedman, J.H.: The elements of statistical learning: Data mining, inference, and prediction, 2nd edn., corr. at 7. printing edn. Springer Series in Statistics. Springer, New York (2001)
Hothorn, T., Bühlmann, P., Kneib, T., Schmid, M., Hofner, B.: Model-based boosting 2.0. J. Mach. Learn. Res. 11, 2109–2113 (2010)
Hothorn, T., Bühlmann, P., Kneib, T., Schmid, M., Hofner, B.: mboost: Model-based boosting (2016). http://CRAN.R-project.org/package=mboost. R package version R package version 2.6-0
Li, C., Cao, Y., Zhang, M., Wang, J., Liu, J., Shi, H., Geng, Y.: Hidden benefits of electric vehicles for addressing climate change. Sci. Rep. 5(9213) (2015). doi:10.1038/srep09213
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Massachusetts (2002)
Steinwart, I., Christmann, A., Jordan, M., Kleinberg, J., Schölkopf, B. (eds.): Support vector machines. Information Science and Statistics. Springer, New York (2008). doi:10.1007/978-0-387-77242-4. http://www.springerlink.com/content/uk1165
Türnau, M.: Befragung von elektrofahrzeug–mieterinnen. Mobilität, Gesellschaft und Technik (2014). http://digital.bib-bvb.de/webclient/DeliveryManager?pid=7179727&custom_att_2=simple_viewer
Vapnik, V.N.: Statistical Learning Theory. A Wiley-Interscience Publication. Wiley, New York (1998). http://www.loc.gov/catdir/description/wiley032/97037075.html
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The E-WALD project and this study have been funded by the Bavarian State Ministry for Economic Affairs and Media, Energy and Technology.
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Kluge, C., Schuster, S., Sellner, D. (2018). Statistics Instead of Stopover—Range Predictions for Electric Vehicles. In: Fink, A., Fügenschuh, A., Geiger, M. (eds) Operations Research Proceedings 2016. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-55702-1_8
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DOI: https://doi.org/10.1007/978-3-319-55702-1_8
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