Electric vehicle charge scheduling using an artificial neural network
Electric vehicle charge scheduling using an artificial neural network
复制标题
使用人工神经网络的电动汽车充电调度
DOI：
10.1109/isgt-asia.2016.7796398
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
G. Town
中科院分区：
文献类型：
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作者：
S. Morsalin;K. Mahmud;G. Town
随着电动汽车与电网的整合，采用机器对机器（M2M）通信的智能计量可能在实时能源管理和控制中发挥重要作用。嵌入先进计量基础设施（AMI）的智能设备可以实时预测能源需求并进行能源定价。本文提出了一种基于人工神经网络的智能决策系统，该系统利用M2M AMI记录的数据进行电动汽车充电调度和负荷管理。该人工神经网络使用家庭用电量和电动汽车能源需求数据进行训练，并用于决定车辆何时应该充电（G2V）或何时可以放电（V2G）。
With the integration of EVs into the power grid, smart metering using machine-to-machine (M2M) communication is likely to play an important role in real-time energy management and control. Smart devices embedded with advanced metering infrastructure (AMI) can forecast the energy demand as well as perform energy pricing in real time. In this paper, an artificial neural network (ANN) based intelligent decision-making system is presented that utilises data logged by an M2M AMI for EV charge scheduling and load management. The ANN was trained using household power consumption and EV energy demand data, and was used to decide when a vehicle should charge (G2V), or could discharge (V2G).