Citation

Azhar, Nayli A. and M. Radzi, Nurul A. and Ramachandaramurthy, Vigna K. and Abdullah, Fairuz and Kahar, Nor H. A. and Awang, Azlan and Choo, Kan Yeep (2025) Electric vehicle consumption dataset tailored to Malaysian situation and implemented using Rapid Miner Auto-Model. Journal of Engineering Science and Technology., 20 (2). pp. 490-509. ISSN 1823-4690

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Abstract

The rising global adoption of electric vehicles (EVs) is directly linked to an increase in the number of EV charging stations (EVCS). Consequently, researchers are more inclined to pursue studies in this field. However, data availability is limited, and obtaining information from relevant organizations or utilities poses significant challenges. In this study, inspired by the ACN-Data, an EV consumption dataset was created tailored to the Malaysian behaviour. Additional parameters were included to account for Malaysian behaviour, including temperature, traffic, number of EVCS available, travel distance per day (km), and EV consumption (kwh/km) based on six car brands. The dataset underwent preprocessing using Tableau Prep Builder and was then visualised using Tableau Desktop. Furthermore, the dataset was inputted into RapidMiner, which then proposed various interconnected machine learning (ML) techniques. A performance evaluation was conducted, comparing it with different ML methods. Overall, the Support Vector Machine has the best performance in term of root mean square error, absolute error, squared error and second best on relative error with 10.876, 1.278, 132.530 and 27.59%, respectively. By developing a dataset uniquely tailored to capture Malaysian behaviour for the first time, we anticipate that it will have broad and impactful applications in the future, driving significant advancements across various sectors.

Item Type:	Article
Uncontrolled Keywords:	Electric vehicle, Electric vehicle consumption, Dataset, Machine learning, RapidMiner, Tableau, Visualisation
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK4001-4102 Applications of electric power
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	12 Mar 2025 01:27
Last Modified:	12 Mar 2025 01:27
URII:	http://shdl.mmu.edu.my/id/eprint/13613

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