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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Wahyu Cesar
National Research and Innovation Agency (BRIN)
Computer Science & IT

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A hybrid ARIMA and DNN approach with residual learning for electric vehicle charging demand forecasting Wahyu Cesar; Dwidharma Priyasta; Prasetyo Aji; Melyana Melyana; Agus Suprianto; Osen Fili Nami; Riza Riza
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27219

Abstract

The rapid growth of electric vehicle (EV) adoption has created significant challenges for power grid management and charging infrastructure planning. Accurate forecasting of EV charging demand is therefore essential to ensure reliable electricity supply and effective station deployment. This study proposes a novel hybrid forecasting framework that combines autoregressive integrated moving average (ARIMA) with deep neural networks (DNN) through a residual learning strategy. In this approach, ARIMA models the linear temporal patterns, while DNN captures the nonlinear residuals, resulting in improved efficiency and predictive accuracy. The proposed hybrid model is one of the first applications of the residual learning approach for EV demand forecasting in Indonesia. Experimental evaluation using real-world daily consumption data shows that the hybrid method achieved the highest prediction accuracy of 98.22%, consistently outperforming single-model baselines. Beyond technical performance, the model can support stakeholders in planning charging infrastructure and help maintain grid stability in rapidly growing EV ecosystems.
Co-Authors Agus Suprianto Dwidharma Priyasta Melyana Melyana Osen Fili Nami Prasetyo Aji Riza Riza