| 梁文茹,郑宇光,李敬如,赵宇尘.基于K-means与LSTM的充电桩负荷概率预测方法[J].电力需求侧管理,2026,28(4):109-114 |
| 基于K-means与LSTM的充电桩负荷概率预测方法 |
| Probability forecasting method for charging pile load based on K-means and LSTM |
| 投稿时间：2026-02-03 修订日期：2026-04-11 |
| DOI：10.3969/j.issn.1009-1831.2026.04.016 |
| 中文关键词: 充电桩 K-means LSTM 负荷概率预测 |
| 英文关键词: charging pile K-means LSTM probabilistic load forecasting |
| 基金项目:国家电网有限公司总部科技项目(5400-202412367A-3-1-KJ) |
|
| 摘要点击次数: 0 |
| 全文下载次数: 0 |
| 中文摘要: |
| 随着电动汽车保有量的快速增长,其充电负荷的随机性与波动性给电网的安全稳定运行带来了显著挑战,因而精准预测其充电负荷成为新型电力系统建设的关键需求。现有研究忽略了不同充电桩服务对象的异质性,导致不同用途电动车充电负荷特征混叠,预测结果难以支撑电网风险决策。针对上述问题,提出一种基于K-means与长短期记忆网络的电动汽车充电负荷概率预测方法。首先,采用K-means聚类将充电桩按负荷模式与主要服务车辆类型划分为私人车用途、公交车用途、公务车用途、出租车用途及其他用途5类集群,消除特征混叠效应;随后,构建基于长短期记忆网络的预测模型,以加权分位数损失函数替代传统均方误差损失,输出10%~90%分位数预测区间的结果。为验证模型有效性,使用某省级电网公司提供的充电桩负荷数据进行实验,结果表明:完成充电桩集群划分后,负荷预测的平均均方误差降低了16.6%。所提方法能够为电网运行的风险评估与调度决策提供有效支撑。 |
| 英文摘要: |
| With the rapid growth in electric vehicle ownership, the stochasticity and volatility of charging loads have posed significant challenges to the secure and stable operation of power grids. Therefore, accurate EV charging load forecasting has become a critical requirement for the development of new-type power systems. Existing studies often overlook the heterogeneity of service objects among different charging piles, resulting in the mixing of charging load characteristics associated with different vehicle usage types and limiting the ability of forecasting results to support grid risk assessment and decision-making. To address this issue, a probabilistic EV charging load forecasting method based on K-means clustering and long short-term memory networks (LSTM) is proposed. First, K-means clustering is employed to classify charging piles into five clusters according to their load patterns and dominant service vehicle types, namely private vehicles, buses, official vehicles, taxis, and other vehicles, thereby mitigating the feature-mixing effect. Subsequently, a LSTM-based forecasting model is constructed, in which a weighted quantile loss function is adopted instead of the conventional mean squared error loss to generate forecasting results within the 10%~90% quantile prediction interval. To validate the effectiveness of the proposed model, experiments are conducted using charging pile load data provided by a provincial power grid company. The results show that, after charging pile clustering, the average mean squared error of load forecasting is reduced by 16.6%. The proposed method can provide effective support for risk assessment and dispatch decision-making in power grid operation. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|