Constructing the prediction intervals (PIs) for electric vehicle (EV) charging demand based on traffic flow information is crucial for the efficient operation of EV charging stations. However, due to the volatile nature of traffic flow, obtaining long-term traffic flow information (e.g., one week in advance) is challenging, particularly for multiple neighbored sites. To address this issue, this work establishes a deep learning prediction framework called Spatiotemporal Periodic Network (STPNet), which utilizes an encoder-decoder architecture. The STPNet incorporates a spatiotemporal and periodic pattern learning technique, and leverages the advantages of convolutional long short-term memory units (ConvLSTM) to quantify the uncertainty of traffic flow. Furthermore, to improve the performance of traffic flow prediction, a spatiotemporal series decomposition strategy based on Seasonal and Trend decomposition using Loess (STL) is employed, and a spatiotemporal PI performance-based loss function is creatively developed in this work. Then, the PIs of the EV charging demand are obtained based on the predicted traffic flow information and an M/M/C/K queuing model. Validated using a real-world dataset, the proposed model has been demonstrated to exhibit effectiveness in generating high-quality EV charging demand PIs for multiple locations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    STPNet: Quantifying the Uncertainty of Electric Vehicle Charging Demand via Long-Term Spatiotemporal Traffic Flow Prediction Intervals


    Beteiligte:
    Li, Yiqun (Autor:in) / Chai, Songjian (Autor:in) / Zhang, Xian (Autor:in) / Wang, Guibin (Autor:in) / Zhu, Rongwu (Autor:in) / Chung, Edward (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2023


    Format / Umfang :

    3994401 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Quantifying the Uncertainty in Long-Term Traffic Prediction Based on PI-ConvLSTM Network

    Li, Yiqun / Chai, Songjian / Wang, Guibin et al. | IEEE | 2022




    SPATIOTEMPORAL TRAFFIC FLOW PREDICTION SYSTEM

    KIM EUN YI | Europäisches Patentamt | 2016

    Freier Zugriff

    Spatial Load Prediction Considering Spatiotemporal Distribution of Electric Vehicle Charging Load

    Gao Xiang / Wei Lingyan / Wang Bing et al. | DOAJ | 2021

    Freier Zugriff