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A Variant of the Growing Neural Gas Algorithm for the Design of an Electric Vehicle Charger Network

Author

Listed:
  • Manuel Curado

    (Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
    These authors contributed equally to this work.)

  • Diego Hidalgo

    (Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
    These authors contributed equally to this work.)

  • Jose L. Oliver

    (Department of Graphic Expression, Composition and Projects, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
    These authors contributed equally to this work.)

  • Leandro Tortosa

    (Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
    These authors contributed equally to this work.)

  • Jose F. Vicent

    (Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
    These authors contributed equally to this work.)

Abstract

The Growing Neural Gas (GNG) algorithm constitutes an incremental neural network model based on the idea of a Self-Organizing Map (SOM), that is, unsupervised learning algorithms that reduce the dimensionality of datasets by locating similar samples close to each other. The design of an electric vehicle charging network is an essential aspect in the transition towards more sustainable and environmentally friendly mobility. The need to design and implement an efficient network that meets the needs of all users motivates us to propose the use of a model based on GNG-type neural networks for the design of the network in a specific geographical area. In this paper, a variant of this iterative neural network algorithm is used with the objective that, from an initial dataset of points in the plane, it calculates a new simplified dataset with the main characteristic that the final set of points maintains the geometric shape and topology of the original set. To demonstrate the capabilities of the algorithm, it is exemplified in a real case, in which the design of an electric vehicle charging network is proposed. This network is built by applying the algorithm, taking as the original set of points the ones formed by the nodes of the gas station network in the geographical area studied. Several tests of running the algorithm for different sizes of the final dataset are performed, showing the differences between the original network and the computationally generated one.

Suggested Citation

  • Manuel Curado & Diego Hidalgo & Jose L. Oliver & Leandro Tortosa & Jose F. Vicent, 2024. "A Variant of the Growing Neural Gas Algorithm for the Design of an Electric Vehicle Charger Network," Mathematics, MDPI, vol. 12(22), pages 1-23, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3485-:d:1516389
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    References listed on IDEAS

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    1. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Dorrell, David G. & Li, Xiaohui & Zhan, Weipeng, 2023. "Operation optimization approaches of electric vehicle battery swapping and charging station: A literature review," Energy, Elsevier, vol. 263(PE).
    2. Zhuang, Ran & Jiang, Difei & Wang, Yuan, 2023. "An approach to optimize building area ratios scheme of urban complex in different climatic conditions based on comprehensive energy performance evaluation," Applied Energy, Elsevier, vol. 329(C).
    Full references (including those not matched with items on IDEAS)

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