@inproceedings{e22299aa94fc418d94d61990a9517b9f,
title = "Heterogeneous components fusion network for load forecasting of charging stations",
abstract = "Accurate load forecasting of charging stations enable managers to reduce the drivers' waiting time and operating costs. But the existing works for spatial-temporal sequence forecasting usually assume the spatial-continuity of signals. However, the recharging scenario, in which the above assumptions are not valid due to the sparse spatial distribution of stations, need further research. To fill the gap, we present a Heterogeneous Components Fusion Network to model dual components sourced from the planned and the unplanned recharging events independently. For planned recharging component, we design a customized transformer to 'looks up' the reference 'memory' for the prediction. And we propose the time-variant graph to model highly dynamic unplanned events. Experiments conducted on a load reading dataset of 120 stations suggest that our model achieves better performance than a series of state-of-the-arts for spatial-temporal sequence prediction problem.",
keywords = "Charging station, Load forecasting, Time-variant graph, Transformer-based memory network",
author = "Kai Li and Cheng Feng and Fei Yu and Tian Xia",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
year = "2019",
month = nov,
day = "3",
doi = "10.1145/3357384.3358073",
language = "英语",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery ",
pages = "2285--2288",
booktitle = "CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management",
address = "美国",
}