A physics-informed and attention-based graph learning approach for
regional electric vehicle charging demand prediction
- URL: http://arxiv.org/abs/2309.05259v2
- Date: Mon, 6 Nov 2023 06:35:11 GMT
- Title: A physics-informed and attention-based graph learning approach for
regional electric vehicle charging demand prediction
- Authors: Haohao Qu, Haoxuan Kuang, Jun Li, Linlin You
- Abstract summary: This paper proposes a novel approach that enables the integration of graph and temporal attention mechanisms for feature extraction.
Evaluation results on a dataset of 18,013 EV charging piles in Shenzhen, China show that the proposed approach, named PAG, can achieve state-of-the-art forecasting performance.
- Score: 7.441576351434805
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Along with the proliferation of electric vehicles (EVs), optimizing the use
of EV charging space can significantly alleviate the growing load on
intelligent transportation systems. As the foundation to achieve such an
optimization, a spatiotemporal method for EV charging demand prediction in
urban areas is required. Although several solutions have been proposed by using
data-driven deep learning methods, it can be found that these
performance-oriented methods may suffer from misinterpretations to correctly
handle the reverse relationship between charging demands and prices. To tackle
the emerging challenges of training an accurate and interpretable prediction
model, this paper proposes a novel approach that enables the integration of
graph and temporal attention mechanisms for feature extraction and the usage of
physic-informed meta-learning in the model pre-training step for knowledge
transfer. Evaluation results on a dataset of 18,013 EV charging piles in
Shenzhen, China, show that the proposed approach, named PAG, can achieve
state-of-the-art forecasting performance and the ability in understanding the
adaptive changes in charging demands caused by price fluctuations.
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