  For a new city that is committed to promoting Electric Vehicles (EVs), it is
significant to plan the public charging infrastructure where charging demands
are high. However, it is difficult to predict charging demands before the
actual deployment of EV chargers for lack of operational data, resulting in a
deadlock. A direct idea is to leverage the urban transfer learning paradigm to
learn the knowledge from a source city, then exploit it to predict charging
demands, and meanwhile determine locations and amounts of slow/fast chargers
for charging stations in the target city. However, the demand prediction and
charger planning depend on each other, and it is required to re-train the
prediction model to eliminate the negative transfer between cities for each
varied charger plan, leading to the unacceptable time complexity. To this end,
we propose the concept and an effective solution of Simultaneous Demand
Prediction And Planning (SPAP): discriminative features are extracted from
multi-source data, and fed into an Attention-based Spatial-Temporal City Domain
Adaptation Network (AST-CDAN) for cross-city demand prediction; a novel
Transfer Iterative Optimization (TIO) algorithm is designed for charger
planning by iteratively utilizing AST-CDAN and a charger plan fine-tuning
algorithm. Extensive experiments on real-world datasets collected from three
cities in China validate the effectiveness and efficiency of SPAP. Specially,
SPAP improves at most 72.5% revenue compared with the real-world charger
deployment.
