Space load forecasting considering distributed energy and electric vehicles

蒯圣宇; 田佳; 台德群; 王加庆; 韩天轮

doi:10.3969/j.issn.1009-1831.2019.01.011

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Space load forecasting considering distributed energy and electric vehicles

更新时间：2026-03-05

- Space load forecasting considering distributed energy and electric vehicles
- Vol. 21, Issue 1, (2019)
- 作者机构：
  
  1. 国网安徽省电力有限公司,合肥,230022
  2. 国网芜湖无为县供电公司,安徽无为,238300
  3. 华北电力大学,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2019.01.011
  CLC：
- Published：2019
- 稿件说明：
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蒯圣宇, 田佳, 台德群, et al. Space load forecasting considering distributed energy and electric vehicles[J]. 2019, 21(1).
DOI：

蒯圣宇, 田佳, 台德群, et al. Space load forecasting considering distributed energy and electric vehicles[J]. 2019, 21(1). DOI： 10.3969/j.issn.1009-1831.2019.01.011.

摘要

相较于传统负荷预测，空间负荷预测更加关注某一局部空间内的负荷分布情况，因而可以更好地确定电气设备的选型与空间布局。分布式能源及电动汽车的飞速发展，使城市空间负荷分布变得更为复杂，采用原有基于时间序列的负荷预测方法可能带来较大误差，不利于城市规划的经济性与可靠性。利用最小二乘支持向量机（least squares support vector machine，LS?SVM）的非线性映射能力，建立了计及分布式能源与电动汽车充电负荷的空间负荷预测模型，并通过我国中部某地区的实际算例验证了所提方法的有效性。

Abstract

Compared with the traditional load forecasting

the spatial load forecasting pays more attention to the load distribution in a certain space

so it can better determine the selection and spatial layout of the electrical equipment. The rapid development of distributed energy and electric vehicles makes the urban spatial load distribution more complex. The original load forecasting method based on time series may bring large error

which is not conducive to the economy and reliability of urban power grid planning.Due to the nonlinear mapping ability of least squares support vector machine

a spatial load forecasting model for distributed and electric vehicle charging load is established. Finally

a practical example in a certain area of central China shows the effectiveness of the proposed method.

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