Short-term load forecasting for the electric bus station based on GRA-DE-SVR

机译：基于GRA-DE-SVR的电动汽车站短期负荷预测

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With large-scale electric vehicles penetrating into power system, the grid will be faced with severe challenges. Accurate charging load forecasting is required to ensure the security and economy of the grid. Firstly, the factors that influence the daily load of electric bus stations are analyzed in this paper. Based on the grey relation theory, samples of similar days are selected to establish SVM prediction model. In order to improve prediction accuracy, differential evolution (DE) algorithm is applied to optimize parameters of SVR model. Through empirical study, the root mean square error (RMSE) of daily load forecasting is 10.85%. Compared with the standard SVM prediction model, the prediction precision of this paper is increased by 1.52%. What's more, the proposed method has better forecasting performance than the other methods.

机译：随着大型电动汽车渗透到电力系统中，电网将面临严峻的挑战。需要准确的充电负荷预测以确保电网的安全性和经济性。首先，分析了影响电动公交车站日负荷的因素。基于灰色关联理论，选择相似日期的样本建立SVM预测模型。为了提高预测精度，采用差分进化算法对SVR模型的参数进行优化。通过实证研究，日负荷预测的均方根误差（RMSE）为10.85％。与标准的SVM预测模型相比，本文的预测精度提高了1.52％。而且，该方法具有比其他方法更好的预测性能。

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来源
《2014 IEEE Innovative Smart Grid Technologies - Asia》|2014年|388-393|共6页
会议地点 Kuala Lumpur(MY)
作者
Xiaobo Xu; Liu Wenxia; Xi Zhou; Tianyang Zhao;
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作者单位

Institute of Electrical Engineering, North China Electric Power University, Beijing, China;

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原文格式 PDF
正文语种 eng
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关键词
Load forecasting; Load modeling; Meteorology; Predictive models; Sociology; Statistics; Support vector machines; Electric vehicles; differential evolution; grey relation analysis; short-term load forecasting; support vector machine;

机译：负荷预测；负载建模；气象;预测模型；社会学;统计;支持向量机；电动汽车；差异进化灰色关联分析；短期负荷预测；支持向量机;

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4. Short-term load forecasting for the electric bus station based on GRA-DE-SVR [C] . Xiaobo Xu, Liu Wenxia, Xi Zhou, Institute of Electrical and Electronics Engineers Innovative Smart Grid Technologies - Asia . 2014

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Short-term load forecasting for the electric bus station based on GRA-DE-SVR

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