Milan Straka, Pasquale De Falco, Gabriella Ferruzzi, Daniela Proto, Gijs van der Poel, Shahab Khormali, Ľuboš Buzna
arXiv 6 Oct 2019 · Statistics — Applications · 2 citations (OpenAlex)
arXiv:1910.02498 · PDF · DOI · OpenAlex · Extracted main text
The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand, loading local power grids but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that is exploiting predictors compiled from GIS data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators measuring the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l-1 penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kuhn, M., Johnson, K (2013) Applied predictive modeling. volume 26 | 0.950 | 7 | 3 | 86% |
| 2 | James, G., Witten, D., Hastie, T., Tibshirani, R (2013) An introduction to statistical learning. volume 112 | 0.874 | 6 | 3 | 67% |
| 3 | Lucas, A., Prettico, G., Flammini, M.G., Kotsakis, E., Fulli, G., Ma… (2018) Indicator-based methodology for assessing ev charging infrastructure using exploratory data analysis | 0.811 | 4 | 2 | 100% |
| 4 | Lucas, A., Barranco, R., Refa, N (2019) Ev idle time estimation on charging infrastructure, comparing supervised machine learning regressions | 0.511 | 2 | 2 | 50% |
| 5 | ElaadNL (2015) Elaadnl | 0.511 | 2 | 2 | 50% |
| 6 | OpenChargeMap (2015) https://openchargemap.org | 0.511 | 2 | 2 | 50% |
| 7 | Oplaadpalen (2015) https://www.oplaadpalen.nl/ | 0.511 | 2 | 2 | 50% |
| 8 | (EIA)., U.E.I.A (2013) International energy outlook 2016 with projections to 2040 | 0.511 | 2 | 1 | 100% |
| 9 | Hastie, T., Tibshirani, R., Friedman, J (2009) The elements of statistical learning: data mining, inference and prediction | 0.511 | 2 | 1 | 100% |
| 10 | Helmus, J., Spoelstra, J., Refa, N., Lees, M., van den Hoed, R (2018) Assessment of public charging infrastructure push and pull rollout strategies: The case of the netherlands | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 68 scored citations.