# SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City

arXiv: https://arxiv.org/abs/2110.09452 · HTML: https://mlsanity.com/paper/2110.09452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction](https://mlsanity.com/paper/1901.08518/) (2019)
- [SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting](https://mlsanity.com/paper/1908.03716/) (2019)
- [Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.07359/) (2021)
- [Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations](https://mlsanity.com/paper/1604.05472/) (2016)
- [Smart City Development with Urban Transfer Learning](https://mlsanity.com/paper/1808.01552/) (2018)
- [Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions](https://mlsanity.com/paper/1310.6925/) (2013)

## Abstract (arXiv)

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.