摘要
开展电动汽车充电负荷预测研究是引导电动汽车进行有序充放电的基础。依据当前广东某电动公交充电站的相关实测数据,对电动公交充电站负荷特征进行分析,提出一种基于小波神经网络(WNN)的电动公交站短期负荷预测方法。利用该方法对随机选取的两组测试日进行预测实例分析,并与单一BP网络模型的预测效果进行比较。统计结果表明,基于WNN的预测方法具有较高的预测精度,满足一定的应用要求,适用于电动公交充电站短期负荷预测。
With the large-scale use of electric vehicles （EVs）, a short-term load forecast method based on wavelet neural network （WNN） for electric buses is proposed to analyze load characteristics in order to better arrange transmission and distribution planning and regulate EVs charging or discharging, which comes from the current measured data related the charging station, Guangdong. This method is used to predicting EVs＇ load data of two test days selected randomly, compared with the effect of the single BP network model. The statistical results show that this prediction method has higher accuracy to meet certain application requirements than BP network applying to short-term load forecast of charging station for electric buses.
出处
《控制工程》
CSCD
北大核心
2016年第11期1725-1729,共5页
Control Engineering of China
基金
湖南省教育厅科学研究项目(14C0272)
湖南省自然科学基金(2015JJ2014)
