{"id":233,"date":"2021-04-27T16:26:51","date_gmt":"2021-04-27T16:26:51","guid":{"rendered":"https:\/\/www.andreapasotti.it\/home\/?p=233"},"modified":"2021-04-27T17:07:50","modified_gmt":"2021-04-27T17:07:50","slug":"ireland-chargepoints","status":"publish","type":"post","link":"https:\/\/www.andreapasotti.it\/home\/statistics\/ireland-chargepoints\/","title":{"rendered":"Studying Electric Chargepoints in Ireland"},"content":{"rendered":"\n

This page is dedicated to the group project for the course of Applied Statistics, by professor Secchi, which I attended in 2019 at the Politecnico di Milano. We were given the task to analyze a huge dataset related to the usage of Charging Stations for electric vehicles in Ireland, and this webpage was designed to summarize our work and present all the obtained results.<\/p>\n\n\n\n

Table of contents:<\/strong><\/h3>\n\n\n\n
  1. Introduction<\/a><\/li>
  2. Variability analysis – ANOVA<\/a><\/li>
  3. MDS (Multi Dimentional Scaling)<\/a><\/li>
  4. Charge point clustering – DBSCAN <\/a><\/li>
  5. Prediction for new data – Kriging<\/a><\/li>
  6. Conclusions<\/a><\/li>
  7. Bonus part<\/a><\/li><\/ol>\n\n\n\n

    Introduction<\/strong><\/h4>\n\n\n\n

    That of electric mobility is becoming a very hot topic nowadays.
    The State of Ireland has invested a lot in it: in 2008 they wanted to diminish the use of fossil fuels and they set the target to have at least 10% of the national car fleet (equivalent to 200000 units) made by e-vehicles by 2020.<\/p>\n\n\n\n

    Unfortunately, their expectations weren’t quite fulfilled and the original target has dropped in 2017, as stated by the Irish Minister for the Environment in these few lines.<\/p>\n\n\n\n

    There are about 5500 EVs in the country, the 0.26% of total licenced cars and it has been predicted that there will be just 8000 EVs on the country\u2019s roads by 2020.<\/em><\/p> Denis Naughten<\/strong> – Minister for Communications, Climate Action and Environment [19 July 2017]<\/em><\/cite><\/blockquote>\n\n\n\n

    To further support the use of elecric cars, Irish government has created a powerful infrastructure of charging stations, made by a total of 343 charge points (that, at the moment, are available free-to-use). <\/p>\n\n\n\n

    \"\"
    Usage of Fast and Slow Charge Points in a specifc day of May 2018<\/figcaption><\/figure><\/div>\n\n\n\n

    As you can see from this picture, there exist two different types of charge points: the standard one that needs from 6 to 8 hours to charge a common e-car and the fast type, that can charge an electric vehicle up to 80% in just 25 minutes.<\/p>\n\n\n\n

    Our goal was to try to understand the strength and weaknesses of this powerful infrastructure of Charging stations, and to understand possible reasons for the overall low usage of them.<\/p>\n\n\n\n

    To do so, we were provided a dataset with all the characteristics of the Charge Points: an ID number, the type (Standard or Fast), a detail for the position location (urban, rural, industrial, motorway, shopping center, commercial), as well as others properties. <\/p>\n\n\n\n

    \"\"
    Charge Point – Position Details<\/figcaption><\/figure><\/div>\n\n\n\n

    For each of the charging stations we then had another dataset, this one explaining the status of each charging unit (1 – currently under use \/ 0 – not used) calculated every 5 minutes for every day of the year 2018.<\/p>\n\n\n\n

    We started from these datasets by aggregating part of them (one of the things that we did, for example, was to evaluate the average annual usage of each charge point) and we performed different statistical analysis.
    This to understand which were the most busy and why. <\/p>\n\n\n\n

    We studied the spatio-temporal patterns of charge points usage in Ireland and the global variability, alongside with possible dependences on different external factors. <\/p>\n\n\n\n

    • \"\"
      Number of Charge Points {by County}<\/figcaption><\/figure><\/li>
    • \"\"
      Population {by County}<\/figcaption><\/figure><\/li><\/ul><\/figure>\n\n\n\n

      In this gallery, you can see how the number of Charge Points per each County is qualitatively correlated to the population of the same County.<\/p>\n\n\n\n

      While making this analysis, we noticed that there were some infrastructures used way less than others, and that the peaks were concentrated around the capital city of Dublin. <\/p>\n\n\n\n

      After these first considerations, we stated the driving questions for all our analysis: what are the optimal positions for new stations? Does it make sense to add new turrets to increase the global use of the whole infrastracture?
      <\/p>\n\n\n\n

      Variability analysis – ANOVA<\/strong><\/h4>\n\n\n\n

      In order to estimate any significant difference of usage between the various locations, we have performed 3 different ANOVA tests for the main subclasses (Type, Position Detail, Area).<\/p>\n\n\n\n

      To build the most valuable model, we estimated the annual usage through 2018\u2019s months in which there haven\u2019t been any complete shutdown of the charge points. We haven\u2019t spotted any particular usage pattern throughout different months, so we focused on the more representative ones. These months were the following: March, April, June, September, October, December.<\/p>\n\n\n\n

      Here some explanatory boxplots for the variable of interest: the average use.<\/p>\n\n\n\n

      \"\"
      Boxplot for the average use
      {annual \/ above specified months}<\/figcaption><\/figure><\/div>\n\n\n\n

      After this, we tested for an overall Gaussianity of our data:<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n

      Then, even if not strictly necessary at this point, we decided to transform them via the Box Cox transformation suggested us by the function powerTransform().<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n

      The first class we\u2019ve analyzed was Fast <\/em>vs Standard <\/em>Charge Points, representing the velocity of the chargepoint.
      Here the boxplot for the data.<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n

      We first tested and verified the hypothesis of gaussianity within groups and homogeneity of variance between groups, and then we fitted the ANOVA model.<\/p>\n\n\n\n

      From this analysis we concluded that there was no evidence to state that it exists a difference in the average usage between Fast<\/em> or Standard <\/em>charge points.
      As previously stated, though, each Fast <\/em>charge point performs three times better than a Standard <\/em>one (on average), so we concluded that Fast<\/em> charge points contribute the most in terms of number of vehicles recharged.<\/p>\n\n\n\n

      <\/div>\n\n\n\n

      The second class of interest we analized, took into account the urban position type:<\/p>\n\n\n\n

      \"\"
      Percentage of usage by location of the Charging Stations
      <\/figcaption><\/figure><\/div>\n\n\n\n

      We tested the model hypothesis over the groups:<\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n
      # Pvalues per each class:
      c1$p <- 0.14586        c2$p <- 0.09752
      c3$p <- 0.77797        c4$p <- 0.36122
      c5$p <- 0.26376<\/pre>\n\n\n\n
      and then we fitted the model:<\/p>\n\n\n\n
      \"\"<\/figure><\/div>\n\n\n\n
      There is evidence over a difference in the average usage, so we proceeded the analysis with the construction of the univariate confidence intervals and the evaluation of the respective p-values of our tests:<\/p>\n\n\n\n
      \"\"<\/figure><\/div>\n\n\n\n
      As we can see, the only class having a significant difference in the average usage is the Motorway position, which has a lower usage.<\/p>\n\n\n\n
      <\/div>\n\n\n\n
      The last class of interest involved the geographical position of the charge points:<\/p>\n\n\n\n
      \"\"
      Normalized boxplots of the percentage of usage by geographical position<\/figcaption><\/figure><\/div>\n\n\n\n
      Again, we verified the hypothesis and fitted the ANOVA model, which gave us this result:<\/p>\n\n\n\n
      \"\"<\/figure><\/div>\n\n\n\n
      There is enough evidence to believe that there is a difference in the average usage between the groups; evaluating the univariate confidence intervals we find a significant difference in the usage between the City group and both the others, as we can see from the p-values for the mean differences between the classes:<\/p>\n\n\n\n
      # {H0: mui=muj  |  H1: H0^c}

      # p
      [1] 0.00422 0.00072 0.37451<\/pre>\n\n\n\n
      We concluded that the average usage of the Charge Points located in big cities is significantly higher than the other areas’, but there is not a significant difference between the  usages of those located in Countries and Towns.
      <\/p>\n\n\n\n
      MDS (Multi Dimentional Scaling<\/strong>)<\/h4>\n\n\n\n
      Now we’ll get back to the study of our dataset in the framework of Geostatistics.
      The first thing we asked ourselves when we started working on the data, was how to be as close to reality as possible in the representation of our Spatial Data.<\/p>\n\n\n\n
      As you might already know, in the most generic problems of Geostatics, it is sufficient to have the coordinates of a certain amount of points (belonging to a geographic domain D), in order to be able to evaluate the distance between each two of them, with a Euclidean approach.
      Our concern, indeed, was that in our specific situation the Euclidean distance wasn\u2019t so much faithful to reality. This because we were studying a problem where the units\/observations were placed into a road network and the Euclidean representation of the problem just did not take this into account.<\/p>\n\n\n\n
      In order to solve this problem, we decided to create a matrix of \u201creal distances\u201d, based on the data provided by the API of Google Maps. 
      {n.d.r. At first we tried to circumvent the problem of creating an account on the Google Maps API Platform by performing the calculation \u201cby hand\u201d on the online website\u2026 And this is when we realized that we had 343 charging stations in our dataset and that 343^2 was a pretty high number, even for the most committed students}<\/p>\n\n\n\n
      After \u201cjust\u201d 6h of the API-code running {n.d.r. And 476$ spent of the beginner bonus provided by Google} we were able to look at our stunning 343×343 matrix.<\/p>\n\n\n\n
      Here we noticed that the notion of \u201cGoogle Distance\u201d wasn\u2019t at all a distance in the mathematical way.
      Apart from the positivity, all other properties weren\u2019t fulfilled:<\/p>\n\n\n\n