http://www.iaeme.com/IJARET/index.asp 195 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 9, September 2020, pp. 195-206, Article ID: IJARET_11_09_020 Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=9 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.9.2020.020 © IAEME Publication Scopus Indexed TIME SERIES FORECASTING OF POWER STATIONS FOR CHARGING ELECTRICAL VEHICLES USING HOT WINTER AND SIMPLE SEASONAL MODEL Dr. B. Gopi Dean, Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, Tamilnadu, India ABSTRACT In recent times, there is huge amount of interest can be seen in the manufacturing of electric vehicles. Because of major development in the energy of the battery, car manufacturers have taken measures in launching affordable electric vehicles on market. In addition to the increasing interest, entire world is experiencing a critical situation in fossil fuel shortage and the resultant environmental consequences of pollution from burning, looking for alternative sources are regarded as major topic contemplated on a global scale. One prominent consumer of energy and also major contributor for air pollution is Transportation. In order to drastically cut down pollution in urban areas, the implementation of Electric Vehicles (EVs) is plausible approach. Also, while considering traditional fossil fuels to that of other promising renewable energies which are most likely employed in forms of acquiring solar energy and tidal energy, electricity can be efficiently transformed. Electric Vehicles is a substitution of traditional internal combustion engine vehicles. Applying environment- friendly strategy from the overall pollutant sources leading indicators that are achieved in offered by EVs. In recent years, quick development of EVs has been seen with the growing recognition of the idea of smart cities. This calls for an efficient use of related supporting facilities, among which charging facility is of top priority. Thus, the EV flow and traffic conditions in the road network are affected by this. It can still take numerous dozens of minutes, even if charging in stations appeared much faster than that of domestic electricity. Therefore, performance with regards to charging system, as mainly queuing state in charging stations are greatly influenced by the EV drivers’ charging behaviour. Using time series algorithm in Python, this paper is concerned with forecasting the actual trend in power stations in the country. This simulation facilitates in well-organized management of charging stations. The interactions among charging stations and EV drivers are studied cautiously to achieve this goal, in addition to bounded rationality of EV drivers in charging activities. To