Milan Straka, Rui Carvalho, Gijs van der Poel, Ľuboš Buzna
arXiv 2 Jun 2020 · Statistics — Applications
arXiv:2006.01672 · PDF · DOI · OpenAlex · Extracted main text
Here, we develop a data-centric approach enabling to analyse which activities, function, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. To gain a basic insight, we analysed the probabilistic distribution of energy consumption and its relation to indicators characterizing charging events. We collected geospatial datasets and utilizing statistical methods for data pre-processing, we prepared features modelling the spatial context in which the charging infrastructure operates. To enhance the statistical reliability of results, we applied the bootstrap method together with the Lasso method that combines regression with variable selection ability. We evaluate the statistical distributions of the selected regression coefficients. We identified the most influential features correlated with energy consumption, indicating that the spatial context of the charging infrastructure affects its utilization pattern. Many of these features are related to the economic prosperity of residents. Application of the methodology to a specific class of charging infrastructure enables the differentiation of selected features, e.g. by the used rollout strategy. Overall, the paper demonstrates the application of statistical methodologies to energy data and provides insights on factors potentially shaping the energy consumption that could be utilized when developing models to inform charging infrastructure deployment and planning of power grids.
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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 | Hastie, T., Tibshirani, R., Wainwright, M (2015) Statistical learning with sparsity: the lasso and generalizations | 0.874 | 9 | 2 | 100% |
| 2 | Hsu, D (2015) Identifying key variables and interactions in statistical models of building energy consumption using regularization | 0.737 | 3 | 2 | 100% |
| 3 | Cazzola, P., Gorner, M., Tattini, J., Schuitmaker, R., Scheffer, S.,… (2019) Global EV Outlook 2019 - Scaling up the transition to electric mobility | 0.693 | 5 | 1 | 100% |
| 4 | 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 | 0.644 | 2 | 2 | 100% |
| 5 | Kuhn, M., Johnson, K (2013) Applied predictive modeling | 0.585 | 5 | 1 | 60% |
| 6 | Warren-Hicks, W.J., Hart, A (2010) Application of uncertainty analysis to ecological risks of pesticides | 0.585 | 3 | 3 | 33% |
| 7 | Netherlands Enterprise Agency (2019) Electric vehicle charging - definitions and explanation | 0.585 | 3 | 1 | 100% |
| 8 | Pevec, D., Babic, J., Kayser, M.A., Carvalho, A., Ghiassi-Farrokhfal… (2018) A data-driven statistical approach for extending electric vehicle charging infrastructure | 0.585 | 3 | 1 | 100% |
| 9 | James, G., Witten, D., Hastie, T., Tibshirani, R (2014) An Introduction to Statistical Learning: With Applications in R | 0.550 | 6 | 1 | 50% |
| 10 | OpenChargeMap (2019) URL: https://openchargemap.org. accessed: 2019-01-10 | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 65 scored citations.