Method and device for simulating spatial demand of automobile energy supplement based on multi-source spatio-temporal data

Abstract

The application discloses a vehicle energy supplement demand space simulation method and device based on multi-source space-time data, and relates to the field of urban planning and traffic energy systems. An unsupervised learning method is used to find patterns in the global grid unit, automatically identify energy supplement demand pattern areas with similar energy supplement demand behaviors and environmental characteristics; a causal analysis dataset is constructed based on each energy supplement demand pattern area, and a causal discovery algorithm is used to infer the core driving factor system; based on the spatial partitioning of the energy supplement demand pattern area, the energy supplement demand characteristics and the core driving factor system of each partition are used to adaptively select the target energy supplement demand prediction model for each partition. The application automatically identifies the core driving factors that have a direct causal effect on energy supplement demand, uses an adaptive modeling method constrained by causal structure in different pattern areas, effectively avoids the failure of a unified model in areas with significant heterogeneity, and improves the stability, interpretability and repeatability of the energy supplement demand space distribution results.

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