基于SAE-ELM的电动汽车充电站负荷预测模型

龚钢军; 安晓楠; 陈志敏; 张帅; 文亚凤; 吴秋新; 苏畅

基于SAE-ELM的电动汽车充电站负荷预测模型

1.
北京市能源电力信息安全工程技术研究中心(华北电力大学),北京 102206;
2.
北京信息科技大学,北京 100192

基金项目: 国家863计划课题(2015AA050203)

详细信息

作者简介:
龚钢军(1974—),男,博士,副教授,研究方向为人工智能、能源电力信息安全、主动配电网,E-mail:gong@ncepu.edu.cn;安晓楠(1993—),女,硕士研究生,研究方向为电力负荷预测、电力系统通信与安全,E-mail:13120425789@163.com。

中图分类号: TM715
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出版历程
- 收稿日期: 2018-11-17
- 发布日期: 2019-12-08

Model of Load Forecasting of Electric Vehicle Charging Station Based on SAE-ELM

1.
Beijing Engineering Research Center of Energy Electric Power Information Security North China Electric Power University,Changping District, Beijing 102206, China;
2.
Beijing Information Science and Technology University, Haidian District, Beijing 100192, China

摘要

摘要: 电动汽车(electric vehicle,EV)用户充电行为在时间和空间上的随机性增加了EV充电站负荷预测的难度,为此以提高负荷预测的准确度为目的,通过改进深度学习中的栈式自编码器提出栈式自编码器-极限学习机(SAE-ELM)的混合模型,并深入研究EV与电网的交互模式;综合考虑影响充电站负荷量的关键因素,如历史负荷、环境、日类型等,对某地充电站进行短期负荷预测并验证;最后与SAE-BP、ELM算法做对比实验,实验结果表明SAE-ELM对充电站的短期负荷预测更加有效准确,更有利于电网稳定运行。
- 电动汽车充电站 /
- 负荷预测 /
- 深度学习 /
- SAE- ELM
Abstract: The randomness of charging behavior in time and space increases the difficulty of load forecasting of EV charging station. In this paper, the stacked auto encoder neural network-extreme learning machine (SAE-ELM) hybrid model is proposed by improving the stack auto encoder of deep-learning to realize short-term load forecasting of charging stations. The interactive mode of electric vehicle and power grid is introduced and the key factors affecting the charging station load, such as historical load, environment, typical day type, etc., are also considered. Finally, the short-term load forecasting of a practical charging station is realized and compared with SAE-BP and ELM algorithm. The result shows the proposed approach can provide more accurate forecasting result, which benefits the stable operation of power grid.
- electric vehicle charging station /
- load forecasting /
- deep-learning /
- SAE-ELM

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基于SAE-ELM的电动汽车充电站负荷预测模型

作者简介: 龚钢军(1974—),男,博士,副教授,研究方向为人工智能、能源电力信息安全、主动配电网,E-mail:gong@ncepu.edu.cn;安晓楠(1993—),女,硕士研究生,研究方向为电力负荷预测、电力系统通信与安全,E-mail:13120425789@163.com。

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出版历程

Model of Load Forecasting of Electric Vehicle Charging Station Based on SAE-ELM

期刊类型引用(12)

其他类型引用(12)

计量

出版历程

目录

作者简介:
龚钢军(1974—),男,博士,副教授,研究方向为人工智能、能源电力信息安全、主动配电网,E-mail:gong@ncepu.edu.cn;安晓楠(1993—),女,硕士研究生,研究方向为电力负荷预测、电力系统通信与安全,E-mail:13120425789@163.com。