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基于電動汽車充電負荷變動速率與TCN-LSTM的負荷預測

來源：電工電氣發(fā)布時間：2025-06-27 13:27 瀏覽次數(shù)：3

基于電動汽車充電負荷變動速率與TCN-LSTM的負荷預測

汪楚皓^1，2，郭航³

(1 長沙理工大學電氣與信息工程學院，湖南長沙 410114；

2 國網(wǎng)湖南省電力有限公司常德供電分公司，湖南常德 415000；

3 國網(wǎng)湖南省電力有限公司株洲供電分公司，湖南株洲 412000)

摘要：電動汽車充電負荷的隨機性波動對電力系統(tǒng)的安全穩(wěn)定性帶來挑戰(zhàn)，提出了一種基于電動汽車充電負荷變動速率與人工智能算法結(jié)合的短期預測方法。分析了電動汽車充電負荷的歷史數(shù)據(jù)，提出了一種反映充電負荷速率變動特征的指標；結(jié)合時空卷積網(wǎng)絡(TCN)和長短期記憶網(wǎng)絡(LSTM)構(gòu)建了預測模型，對充電負荷進行精準預測。實驗結(jié)果表明，該方法能夠有效研究區(qū)域內(nèi)電動汽車用戶的充電規(guī)律，對充電負荷峰谷態(tài)勢的預測表現(xiàn)出較高的準確性，為深入分析用戶充電行為模式、準確預估短期充電負荷提供了重要技術(shù)支持，對提升電力系統(tǒng)運行效率與穩(wěn)定性具有重要意義。

關(guān)鍵詞： 電動汽車；充電負荷；時空卷積網(wǎng)絡；長短期記憶網(wǎng)絡；短期負荷預測

中圖分類號：TM715 ；U469.72 文獻標識碼：A 文章編號：1007-3175(2025)06-0019-05

Load Forecasting Based on the Variation Rate of Electric

Vehicle Charging Load and TCN-LSTM

WANG Chu-hao^1,2, GUO Hang³

（1 School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China;

2 State Grid Hunan Electric Power Co., Ltd. Changde Power Supply Branch Company, Changde 415000, China;

3 State Grid Hunan Electric Power Co., Ltd. Zhuzhou Power Supply Branch Company, Zhuzhou 412000, China）

Abstract: The stochastic fluctuations of electric vehicle charging loads pose challenges to the safety and stability of power systems. To address this issue, this paper proposes a short-term forecasting method based on the combination of the variation rate of electric vehicle charging load and artificial intelligence algorithms. Firstly, historical data of electric vehicle charging loads are analyzed, and an indicator reflecting the variation characteristics of the charging load rate is introduced. Subsequently, a predictive model combining temporal convolutional network(TCN) and long short-term memory network(LSTM) is constructed to achieve accurate load forecasting. Experimental results demonstrate that the proposed method effectively analyzes the charging patterns of electric vehicle users in the study area and achieves high accuracy in predicting the peak-valley trends of charging loads. This study provides critical technical support for analyzing user charging behavior patterns and accurately estimating short-term charging loads, offering significant contributions to enhancing the operational efficiency and stability of power systems.

Key words: electric vehicle; charging load; temporal convolutional network; long short-term memory network; short-term load forecasting

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基于電動汽車充電負荷變動速率與TCN-LSTM的負荷預測