Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles

نویسندگان

چکیده

In recent years, the supply of electric vehicles, which are eco-friendly cars that use energy rather than fossil fuels, cause air pollution, is increasing. Accordingly, it emerging as an urgent task to predict charging demand for smooth required charge vehicle batteries. this paper, demand, time series analysis performed based on two types frames: One using traditional techniques such dynamic harmonic regression, seasonal and trend decomposition Loess, Bayesian structural series. The other most widely used machine learning techniques, including random forest extreme gradient boosting. However, tree-based approaches have disadvantage not being able capture trend, so a hybrid strategy proposed overcome problem. addition, variation reflected feature by Fourier transform useful in case describing seasonality patterns data with multiple seasonality. considered models compared evaluated through various accuracy measures. experimental results show approach generally achieves significant improvements predicting demand. Moreover, when original method, prediction more accurate method. Based these results, can find out smoothly planning future power efficiently managing electricity grids.

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ژورنال

عنوان ژورنال: Sustainability

سال: 2022

ISSN: ['2071-1050']

DOI: https://doi.org/10.3390/su14095426