Attention-based Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences

• URL: http://arxiv.org/abs/2410.18766v1
• Date: Thu, 24 Oct 2024 14:19:38 GMT
• Title: Attention-based Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences
• Authors: Haoxuan Kuang, Kunxiang Deng, Linlin You, Jun Li,
• Abstract summary: We propose an attention-based heterogeneous data fusion approach (ADF) for electric vehicle charging demand prediction. To learn non-pairwise relationships, we cluster service areas by the types and numbers of points of interest in the areas. We demonstrate the impact of dynamic influences on prediction results in different areas of the city and the effectiveness of our clustering method.
• Score: 5.687001127686438
• License:
• Abstract: Electric vehicle charging demand prediction is important for vacant charging pile recommendation and charging infrastructure planning, thus facilitating vehicle electrification and green energy development. The performance of previous spatio-temporal studies is still far from satisfactory because the traditional graphs are difficult to model non-pairwise spatial relationships and multivariate temporal features are not adequately taken into account. To tackle these issues, we propose an attention-based heterogeneous multivariate data fusion approach (AHMDF) for citywide electric vehicle charging demand prediction, which incorporates geo-based clustered hypergraph and multivariate gated Transformer to considers both static and dynamic influences. To learn non-pairwise relationships, we cluster service areas by the types and numbers of points of interest in the areas and develop attentive hypergraph networks accordingly. Graph attention mechanisms are used for information propagation between neighboring areas. Additionally, we improve the Transformer encoder utilizing gated mechanisms so that it can selectively learn dynamic auxiliary information and temporal features. Experiments on an electric vehicle charging benchmark dataset demonstrate the effectiveness of our proposed approach compared with a broad range of competing baselines. Furthermore, we demonstrate the impact of dynamic influences on prediction results in different areas of the city and the effectiveness of our clustering method.

Related papers

• A Nested Graph Reinforcement Learning-based Decision-making Strategy for Eco-platooning [15.543020132680997]
  In large-scale mixed platoons, vehicle heterogeneity and unpredictable traffic conditions lead to virtual bottlenecks. We introduce a decision-making strategy based on nested graph reinforcement learning. This strategy improves collaborative decision-making, ensuring energy efficiency and alleviating congestion.
  arXiv  Detail & Related papers  (2024-08-14T14:18:51Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction [7.441576351434805]
  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.
  arXiv  Detail & Related papers  (2023-09-11T06:31:45Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Holistic Graph-based Motion Prediction [2.365702128814616]
  We present a novel approach for a graph-based motion prediction based on a heterogeneous holistic graph representation. The information is encoded through different types of nodes and edges that both are enriched with arbitrary features.
  arXiv  Detail & Related papers  (2023-01-31T10:46:46Z)
• AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting [8.596556653895028]
  Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study. Our model is tested on the data collected in Dundee City.
  arXiv  Detail & Related papers  (2022-09-07T13:51:45Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)
• Learning Geo-Contextual Embeddings for Commuting Flow Prediction [20.600183945696863]
  Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios. We propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction.
  arXiv  Detail & Related papers  (2020-05-04T17:45:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.