14 International Journal for Modern Trends in Science and Technology Development of Electric Vehicle Charging Infrastructure Based on Population S.Mani 1 | R.Raguraj 1 | R.Harikaran 1 | S.Hariramselvakanth 1 | K.S.Gowthaman 2 1 UG Scholar, Department of EEE, Government College of Engineering, Sengipatti, Thanjavur-613402, Tamilnadu, India. 2 Assistant Professor, Department of EEE, Government College of Engineering, Sengipatti, Thanjavur-613402, Tamilnadu, India. To Cite this Article S.Mani, R.Raguraj, R.Harikaran and S.Hariramselvakanth and K.S.Gowthaman, “Development of Electric Vehicle Charging Infrastructure Based on Population”, International Journal for Modern Trends in Science and Technology, Vol. 06, Issue 06, June 2020, pp.:14-16; https://doi.org/10.46501/IJMTST060604 Article Info Received on 26-April-2020, Revised on 22-May-2020, Accepted on 25-May-2020, Published on 28-May-2020. This research investigates electric vehicle(EV) charging behavior and aims to find the best method for its prediction in order to optimize the EV charging station(CS). This paper discusses several commonly used machine learning algorithm or k-Nearest Neighbor(k-NN) to predict charging station based on population data records. According to the objective of the charging station planning, use the concept of group to do clustering evolution search. Hence the results of k-NN algorithm achieved through MATLAB software. Based on the population, the initial time location of the charging station will be randomly considered in Manapparai, Lalgudi, Vaiyampatti, Thiruverumbur in Trichy based on population. KEYWORDS: Electric Vehicle(EV), Charging Station(CS), k-Nearest Neighbor(k-NN), MATLAB. Copyright © 2014-2020 International Journal for Modern Trends in Science and Technology DOI: https://doi.org/10.46501/IJMTST060604 I. INTRODUCTION The Indian government has recently started taking many initiatives for development of sustainable and easily accessible charging stations. Inappropriate positioning of charging stations may affect smooth operation of the power grid so voltage instability, increased power loss, harmonic and lower reliability indices. In this project, k-NN algorithm are utilized to find the better placement charging station in our area i.e. Manapparai, Lalgudi, Vaiyampatti, Thiruverumbur in Trichy. In k-NN algorithm, the best result is chosen for the better placement of charging station based on population in those areas. II. K-NEAREST NEIGHBOR (K-NN) k-Nearest Neighbor(k-NN) was introduced by Onel Harrison. It is simple and easy to implement machine learning algorithm that can be used to clear regression and classification problems. It is a best method of clustering. Also a random search algorithm based on the concept of natural selection process which is used in optimization problems. In that paper we considered population based charging station. It is easy to implement and by adjusting the parameters the best result is achieved as compared to Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) in some criteria’s only. It becomes makes efficient to use ABSTRACT Available online at: http://www.ijmtst.com/vol6issue06.html International Journal for Modern Trends in Science and Technology ISSN: 2455-3778 :: Volume: 06, Issue No: 06, June 2020