Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network.

Accurate forecasting of traffic patterns plays a crucial role in the effective management and planning of urban transportation infrastructure. In particular, predicting the availability of electric vehicle (EV) charging stations is essential for alleviating range anxiety among drivers and facilitating the adoption of electric vehicles. This study proposes a novel deep learning-based predictor model to approximate the demand for charging electric vehicles over the long term. The methodology integrates the Berkeley wavelet transform (BWT) to decompose input time series data while preserving its inherent characteristics. The proposed hybrid prediction model combines an enhanced gate recurrent unit with an optimized convolution kernel within a fusion graph convolutional network (GCN). The Red Kite Optimization Algorithm (RKOA) is employed to select the convolution kernel of the GCN effectively. Additionally, the construction of the graph leverages both adjacency and adaptive graphs to accurately represent the correlations among nodes in the EV network. The model extracts multi-level spatial correlations through stacked fusion graph convolutional elements and captures multi-scale temporal correlations via an improved gated recurrent unit. Furthermore, the incorporation of residual connection units allows for the fusion of extracted spatiotemporal features with direct data, enhancing predictive performance. The proposed neural predictor is evaluated using EV charging data from Georgia Tech in Atlanta, USA. The experimental results demonstrate the effectiveness of the prediction metrics generated by the proposed model compared to existing methods reported in the literature, showcasing its capability to accurately forecast EV charging demand.Article highlights: In this research work, a novel deep learning (DL)-based predictor model is attempted to be developed for charging electric vehicles. To suggests a hybrid prediction model that is built on an upgraded gate recurrent unit and an optimised convolution kernel of a fusion graph convolutional network (GCN). Red Kite Optimisation Algorithm (RKOA) selects the convolution kernel of the GCN optimally. The outcomes demonstrate the effectiveness of the prediction metrics calculated using the suggested neural predictor for the examined dataset when compared to earlier methods from published studies. [ABSTRACT FROM AUTHOR]