Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations
Authors: Ragavendran Gopalakrishnan, Arpita Biswas, Alefiya Lightwala, Skanda Vasudevan, Partha Dutta, Abhishek Tripathi
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data. |
| Researcher Affiliation | Collaboration | Xerox Research Center India Ragavendran.Gopalakrishnan@xerox.com, Arpita.Biswas@xerox.com, alefiya.lightwala90@gmail.com, skandavs@cse.iitm.ac.in, Partha.Dutta@xerox.com, abhishektripathi.at@gmail.com |
| Pseudocode | Yes | Algorithm 1 IPAC |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | MDR is evaluated on EV charging data obtained from 252 public charging points in North East England through UK s Plugged-In-Places program [Office for Low Emission Vehicles, 2013]. The location of charging points was obtained from UK s National Charge Point Registry data [Office for Low Emission Vehicles, 2012]. Po I information is extracted from Open Street Map [Haklay and Weber, 2008] API for 11 categories... Finally, we use traffic data for each junction-to-junction link on major road networks, provided by [Department for Transport, 2013]. |
| Dataset Splits | Yes | MDR is evaluated in a leave-one-out cross-validation manner by training on all but one instance (charging point) and testing on the left-out instance. |
| Hardware Specification | Yes | Intel(R) Xeon (R) CPU @ 2.2 GHz (16 cores), 32GB RAM, 64-bit Windows. |
| Software Dependencies | Yes | [CPLEX, 2009] IBM ILOG CPLEX. V12. 1: Users manual for CPLEX. |
| Experiment Setup | Yes | We assumed a Level 2 charging rate of 6.4k W and set, for each candidate site, Ni to be the minimum number of Level 2 charging spots necessary (using a queueing model) to ensure that the average peak-demand waiting time (taken as the estimated maximum hourly demand at the candidate site over two years) is less than 5 minutes. for both KP() and SC(), we choose the well-known greedy approximation algorithms introduced in [Vazirani, 2001], and for RANK(), we use the function proposed in Section 3.2. |