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• W4307295673 abstract "From the perspective of electricity distribution networks and the energy system, the increasing numbers of electric vehicles are among the most topical and challenging problems. The paper investigates a novel approach of a convolutional neural network-based modeling method for estimating the spatial distribution of electric vehicles. The proposed model extracts features from multilayer socioeconomic input raster data that are sequenced in strides and outputs a spatial estimation of EV distribution. Spatial forecasting or area forecasting is at the core of the distribution system operators’ planning and development process as it provides a solid foundation for stochastic load modeling and load development analysis. Present models mostly focus on stochastic load modeling, lacking the spatial forecasting aspect of EV distribution. The proposed model aims to enhance EV load modeling by providing a more accurate spatial approach to the models. The study uses large actual socioeconomic and vehicle registration data sets to tackle the modeling challenge. In comparison with previous studies on similar topics, the present study benefits from more samples resulting from an increase in the adoption of electric vehicles. The proposed model architecture performs adequately in predicting a spatial electric vehicle distribution; the CNN model reached a weighted average precision score of 0.91. The proposed methodology greatly enhances stochastic EV load modeling by providing a good spatial forecast of the initial EV locations, and the results can be further aggregated to support the electricity distribution system planning process. An energy-, material-, and cost-efficient electricity distribution system is the backbone of the modern energy system." @default.
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• W4307295673 date "2022-12-01" @default.
• W4307295673 modified "2023-09-26" @default.
• W4307295673 title "Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning" @default.
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• W4307295673 doi "https://doi.org/10.1016/j.apenergy.2022.120124" @default.
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