[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
research-article

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

Published: 24 February 2023 Publication History

Abstract

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 design 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.

References

[1]
2020. China’s public charging pile industry research report. (2020). Retrieved October 2022 from http://report.iresearch.cn/report/202006/3583.shtml.
[2]
Rumen Andonov, Vincent Poirriez, and Sanjay Rajopadhye. 2000. Unbounded knapsack problem: Dynamic programming revisited. European Journal of Operational Research 123, 2 (2000), 394–407.
[3]
T. Donna Chen, Kara M. Kockelman, and Moby Khan. 2013. Locating electric vehicle charging stations: Parking-based assignment method for Seattle, Washington. Transportation Research Record 2385, 1 (2013), 28–36.
[4]
Jingtao Ding, Guanghui Yu, Yong Li, Depeng Jin, and Hui Gao. 2019. Learning from hometown and current city: Cross-city POI recommendation via interest drift and transfer learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4 (2019), 1–28.
[5]
Bowen Du, Yongxin Tong, Zimu Zhou, Qian Tao, and Wenjun Zhou. 2018. Demand-aware charger planning for electric vehicle sharing. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1330–1338.
[6]
Inês Frade, Anabela Ribeiro, Gonçalo Gonçalves, and António Pais Antunes. 2011. Optimal location of charging stations for electric vehicles in a neighborhood in Lisbon, Portugal. Transportation Research Record 2252, 1 (2011), 91–98.
[7]
Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In Proceedings of the International Conference on Machine Learning. 1180–1189.
[8]
Junyu Gao, Qi Wang, and Yuan Yuan. 2019. SCAR: Spatial-/channel-wise attention regression networks for crowd counting. Neurocomputing 363, C (2019), 1–8.
[9]
Ragavendran Gopalakrishnan, Arpita Biswas, Alefiya Lightwala, Skanda Vasudevan, Partha Dutta, and Abhishek Tripathi. 2016. Demand prediction and placement optimization for electric vehicle charging stations. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 3117–3123.
[10]
Bin Guo, Jing Li, Vincent W. Zheng, Zhu Wang, and Zhiwen Yu. 2018. Citytransfer: Transferring inter-and intra-city knowledge for chain store site recommendation based on multi-source urban data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (2018), 1–23.
[11]
Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Li Song, Hui He, and Yu Zheng. 2020. What is the human mobility in a new city: Transfer mobility knowledge across cities. In Proceedings of The Web Conference 2020. 1355–1365.
[12]
IEA. 2020. Global EV Outlook 2020. Retrieved October 2022 from https://www.iea.org/reports/global-ev-outlook-2020.
[13]
IEA. 2021. Global EV Outlook 2021. Retrieved October 2022 from https://www.iea.org/reports/global-ev-outlook-2021.
[14]
Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167. Retrieved from https://arxiv.org/abs/1502.03167.
[15]
Long Jia, Zechun Hu, Yonghua Song, and Zhuowei Luo. 2012. Optimal siting and sizing of electric vehicle charging stations. In Proceedings of the 2012 IEEE International Electric Vehicle Conference. 1–6.
[16]
Mehdi Katranji, Etienne Thuillier, Sami Kraiem, Laurent Moalic, and Fouad Hadj Selem. 2016. Mobility data disaggregation: A transfer learning approach. In Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems. 1672–1677.
[17]
Albert Y. S. Lam, Yiu-Wing Leung, and Xiaowen Chu. 2014. Electric vehicle charging station placement: Formulation, complexity, and solutions. IEEE Transactions on Smart Grid 5, 6 (2014), 2846–2856.
[18]
Yanhua Li, Jun Luo, Chi-Yin Chow, Kam-Lam Chan, Ye Ding, and Fan Zhang. 2015. Growing the charging station network for electric vehicles with trajectory data analytics. In Proceedings of the 2015 IEEE 31st International Conference on Data Engineering. 1376–1387.
[19]
Chen Liu, Ke Deng, Chaojie Li, Jianxin Li, Yanhua Li, and Jun Luo. 2016. The optimal distribution of electric-vehicle chargers across a city. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining. 261–270.
[20]
Qiyu Liu, Yuxiang Zeng, Lei Chen, and Xiuwen Zheng. 2019. Social-aware optimal electric vehicle charger deployment on road network. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 398–407.
[21]
Zhaoyang Liu, Yanyan Shen, and Yanmin Zhu. 2018. Where will dockless shared bikes be stacked? —Parking hotspots detection in a new city. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 566–575.
[22]
Zi-fa Liu, Wei Zhang, Xing Ji, and Ke Li. 2012. Optimal planning of charging station for electric vehicle based on particle swarm optimization. In Proceedings of the IEEE PES Innovative Smart Grid Technologies. 1–5.
[23]
Man Luo, Bowen Du, Konstantin Klemmer, Hongming Zhu, Hakan Ferhatosmanoglu, and Hongkai Wen. 2020. D3P: Data-driven demand prediction for fast expanding electric vehicle sharing systems. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1–21.
[24]
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9 (2008), 2579–2605.
[25]
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf. 2013. Domain generalization via invariant feature representation. In Proceedings of the International Conference on Machine Learning. 10–18.
[26]
Robert M. Nauss. 1978. The 0–1 knapsack problem with multiple choice constraints. European Journal of Operational Research 2, 2 (1978), 125–131.
[27]
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang. 2010. Domain adaptation via transfer component analysis. IEEE IEEE Transactions on Neural Networks 22, 2 (2010), 199–210.
[28]
Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2009), 1345–1359.
[29]
David Sculley. 2010. Combined regression and ranking. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 979–988.
[30]
Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf. 2007. A Hilbert space embedding for distributions. In Proceedings of the International Conference on Algorithmic Learning Theory. Springer, 13–31.
[31]
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 56 (2014), 1929–1958.
[32]
Kang Miao Tan, Vigna K. Ramachandaramurthy, and Jia Ying Yong. 2016. Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. Renewable and Sustainable Energy Reviews 53 (2016), 720–732. DOI:DOI:
[33]
Guang Wang, Xiaoyang Xie, Fan Zhang, Yunhuai Liu, and Desheng Zhang. 2018. bCharge: Data-driven real-time charging scheduling for large-scale electric bus fleets. In Proceedings of the 2018 IEEE Real-Time Systems Symposium. 45–55.
[34]
Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang. 2019. Cross-city transfer learning for deep spatio-temporal prediction. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 1893–1899.
[35]
Leye Wang, Bin Guo, and Qiang Yang. 2018. Smart city development with urban transfer learning. IEEE Computer 51, 12 (2018), 32–41.
[36]
Ying Wei, Yu Zheng, and Qiang Yang. 2016. Transfer knowledge between cities. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1905–1914.
[37]
Yanhai Xiong, Jiarui Gan, Bo An, Chunyan Miao, and Ana LC Bazzan. 2017. Optimal electric vehicle fast charging station placement based on game theoretical framework. IIEEE Transactions on Intelligent Transportation Systems 19, 8 (2017), 2493–2504.
[38]
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In Proceedings of the World Wide Web Conference (WWW’19). Association for Computing Machinery, New York, NY, 2181–2191.
[39]
Weijia Zhang, Hao Liu, Fan Wang, Tong Xu, Haoran Xin, Dejing Dou, and Hui Xiong. 2021. Intelligent electric vehicle charging recommendation based on multi-agent reinforcement learning. In Proceedings of the Web Conference 2021. Association for Computing Machinery, New York, NY, 1856–1867.
[40]
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), 1–55.

Cited By

View all
  • (2024)CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge GraphACM Transactions on Knowledge Discovery from Data10.1145/364356518:5(1-56)Online publication date: 28-Feb-2024
  • (2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
  • (2024)Relevance Feedback with Brain SignalsACM Transactions on Information Systems10.1145/363787442:4(1-37)Online publication date: 9-Feb-2024
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data  Volume 17, Issue 4
May 2023
364 pages
ISSN:1556-4681
EISSN:1556-472X
DOI:10.1145/3583065
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2023
Online AM: 04 October 2022
Accepted: 17 September 2022
Revised: 21 June 2022
Received: 22 January 2022
Published in TKDD Volume 17, Issue 4

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Urban transfer learning
  2. demand prediction
  3. infrastructure planning
  4. electric vehicles

Qualifiers

  • Research-article

Funding Sources

  • National Key Research and Development Program of China
  • National Natural Science Foundation of China
  • Funds for International Cooperation and Exchange of NSFC
  • 111 Project
  • Fundamental Research Funds for the Central Universities
  • BUPT Excellent Ph.D. Students Foundation

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)123
  • Downloads (Last 6 weeks)10
Reflects downloads up to 09 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge GraphACM Transactions on Knowledge Discovery from Data10.1145/364356518:5(1-56)Online publication date: 28-Feb-2024
  • (2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
  • (2024)Relevance Feedback with Brain SignalsACM Transactions on Information Systems10.1145/363787442:4(1-37)Online publication date: 9-Feb-2024
  • (2024)Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation SystemACM Transactions on Intelligent Systems and Technology10.1145/363527315:2(1-26)Online publication date: 28-Mar-2024
  • (2024)Prediction of Energy Delivered by Rapid and Ultra-Rapid Electric Vehicle Chargers: Comparison Between Italy and Germany2024 International Conference on Smart Energy Systems and Technologies (SEST)10.1109/SEST61601.2024.10694446(1-6)Online publication date: 10-Sep-2024
  • (2024)CEL: Cost-Aware Edge-Assisted Livecast via Optimization With Shapley ValueIEEE Internet of Things Journal10.1109/JIOT.2023.332044211:5(7805-7816)Online publication date: 1-Mar-2024
  • (2023)A novel crowdsensing system architecture model and its implementation methodsSCIENTIA SINICA Informationis10.1360/SSI-2022-015753:7(1262)Online publication date: 30-Jun-2023
  • (2023)Multi-aspect Graph Contrastive Learning for Review-enhanced RecommendationACM Transactions on Information Systems10.1145/361810642:2(1-29)Online publication date: 8-Nov-2023
  • (2023)Reinforced PU-learning with Hybrid Negative Sampling Strategies for RecommendationACM Transactions on Intelligent Systems and Technology10.1145/358256214:3(1-25)Online publication date: 8-May-2023
  • (2023)Learn to be Fair without Labels: A Distribution-based Learning Framework for Fair RankingProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605132(23-32)Online publication date: 9-Aug-2023
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media