A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Forecasting and managing urban EV charging demand with spatiotemporal graph models
The rapid proliferation of electric vehicles (EVs) necessitates advanced infrastructural adaptations to accommodate increased demand. This paper introduces a novel dynamic spatiotemporal graph neural network (D-ST-GNN) model designed to optimize the placement and management of EV charging stations in urban environments. Leveraging real-time data and graph theory, the D-ST-GNN model dynamically represents the evolving urban EV charging landscape, incorporating factors such as traffic patterns, station connectivity, and user demand. Employing a synthesized dataset that simulates the interactions of 1000 charging stations across a metropolitan area, our model integrates diverse data sources including traffic flow, weather conditions, and user interactions. The model processes these data through advanced machine learning techniques, utilizing graph neural networks (GNNs) to capture the complex dependencies within the network. The predictive capability of the model was rigorously tested over a 30-day period, demonstrating a mean absolute error of 7.5 kW and a root mean square error of 10 kW, affirming its high accuracy and reliability in forecasting daily charging demands. Significantly, the model employs real-time adaptive algorithms that recalibrate the network based on observed data, enhancing the predictive accuracy over time. Through simulation, the model has shown potential to reduce queue times at charging stations by up to 30% during peak hours and increase overall network efficiency by 25%. The practical application of the D-ST-GNN model provides urban planners and policymakers with a robust tool for making informed decisions regarding the expansion and optimization of EV charging infrastructures.
Forecasting and managing urban EV charging demand with spatiotemporal graph models
The rapid proliferation of electric vehicles (EVs) necessitates advanced infrastructural adaptations to accommodate increased demand. This paper introduces a novel dynamic spatiotemporal graph neural network (D-ST-GNN) model designed to optimize the placement and management of EV charging stations in urban environments. Leveraging real-time data and graph theory, the D-ST-GNN model dynamically represents the evolving urban EV charging landscape, incorporating factors such as traffic patterns, station connectivity, and user demand. Employing a synthesized dataset that simulates the interactions of 1000 charging stations across a metropolitan area, our model integrates diverse data sources including traffic flow, weather conditions, and user interactions. The model processes these data through advanced machine learning techniques, utilizing graph neural networks (GNNs) to capture the complex dependencies within the network. The predictive capability of the model was rigorously tested over a 30-day period, demonstrating a mean absolute error of 7.5 kW and a root mean square error of 10 kW, affirming its high accuracy and reliability in forecasting daily charging demands. Significantly, the model employs real-time adaptive algorithms that recalibrate the network based on observed data, enhancing the predictive accuracy over time. Through simulation, the model has shown potential to reduce queue times at charging stations by up to 30% during peak hours and increase overall network efficiency by 25%. The practical application of the D-ST-GNN model provides urban planners and policymakers with a robust tool for making informed decisions regarding the expansion and optimization of EV charging infrastructures.
Forecasting and managing urban EV charging demand with spatiotemporal graph models
Xi, Zeli (author) / Guo, Guowei (author) / Yang, Xinsen (author) / Sun, Jian (author) / Shi, Xuntao (author) / Xiao, Xiaobing (author)
2025-01-01
10 pages
Article (Journal)
Electronic Resource
English
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