Predicting and Forecasting of Vehicle Charging Station Using ECNN with DHFO Algorithm

Rosebell Paul () and Mercy Paul Selvan
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Rosebell Paul: Department of Computer Science and Engineering, School of Computing, Sathyabhama Institute of Science and Technology, Chennai 600119, India
Mercy Paul Selvan: Department of Computer Science and Engineering, School of Computing, Sathyabhama Institute of Science and Technology, Chennai 600119, India

Energies, 2024, vol. 17, issue 17, 1-25

Abstract: The forecast of the optimal placement of a charging station (CS) according to the real-time consumption of electric vehicles is a subject of urgency in this new era. The demand of a charging station in an area based on the trend of consumption can be predicted by means of interpolation and the extrapolation of historical data using a linear function of prediction model. The prediction of the charging station system was performed with distance relevancy methods. An adaptive optimal learning model was proposed to enhance the prediction performance for charging station management and to represent the pattern of vehicles’ travelling directions. The proposed model uses Distributional Homogeneity Feature Optimization (DHFO) using artificial intelligence (AI) to categorize and forecast the charging station from the database. The prediction performance of this model is improved more than the conventional classification model by filtering the apt features from all the electric vehicular and charging station attributes in the database. The Enhanced Cladistic Neural Network (ECNN) is used to improve the pattern learning model and increase learning accuracy. By comparing statistical parameters with other state-of-the-art methodologies, the suggested model’s overall findings were verified.

Keywords: vehicle charging station prediction; forecasting data; Distributional Homogeneity Feature Optimization (DHFO); Enhanced Cladistic Neural Network (ECNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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