日本成人社区,中文字幕1区2区,亚洲色域网

基于電動(dòng)汽車充電負(fù)荷變動(dòng)速率與TCN-LSTM的負(fù)荷預(yù)測(cè)

來(lái)源：電工電氣發(fā)布時(shí)間：2025-06-27 13:27瀏覽次數(shù)：136

基于電動(dòng)汽車充電負(fù)荷變動(dòng)速率與TCN-LSTM的負(fù)荷預(yù)測(cè)

汪楚皓^1，2，郭航³

(1 長(zhǎng)沙理工大學(xué) 電氣與信息工程學(xué)院，湖南長(zhǎng)沙 410114；

2 國(guó)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(guó)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(dòng)汽車充電負(fù)荷的隨機(jī)性波動(dòng)對(duì)電力系統(tǒng)的安全穩(wěn)定性帶來(lái)挑戰(zhàn)，提出了一種基于電動(dòng)汽車充電負(fù)荷變動(dòng)速率與人工智能算法結(jié)合的短期預(yù)測(cè)方法。分析了電動(dòng)汽車充電負(fù)荷的歷史數(shù)據(jù)，提出了一種反映充電負(fù)荷速率變動(dòng)特征的指標(biāo)；結(jié)合時(shí)空卷積網(wǎng)絡(luò)(TCN)和長(zhǎng)短期記憶網(wǎng)絡(luò)(LSTM)構(gòu)建了預(yù)測(cè)模型，對(duì)充電負(fù)荷進(jìn)行精準(zhǔn)預(yù)測(cè)。實(shí)驗(yàn)結(jié)果表明，該方法能夠有效研究區(qū)域內(nèi)電動(dòng)汽車用戶的充電規(guī)律，對(duì)充電負(fù)荷峰谷態(tài)勢(shì)的預(yù)測(cè)表現(xiàn)出較高的準(zhǔn)確性，為深入分析用戶充電行為模式、準(zhǔn)確預(yù)估短期充電負(fù)荷提供了重要技術(shù)支持，對(duì)提升電力系統(tǒng)運(yùn)行效率與穩(wěn)定性具有重要意義。

關(guān)鍵詞： 電動(dòng)汽車；充電負(fù)荷；時(shí)空卷積網(wǎng)絡(luò)；長(zhǎng)短期記憶網(wǎng)絡(luò)；短期負(fù)荷預(yù)測(cè)

中圖分類號(hào)：TM715 ；U469.72 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：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

參考文獻(xiàn)

[1] HU Xiaosong, YUAN Hao, ZOU Changfu, et al.Coestimation of state of charge and state of health for lithium-ion batteries based on fractionalorder calculus[J].IEEE Transactions on Vehicular Technology，2018，67(11) ：10319-10329.

[2] LIU Senyi, LIU Chunhua.Generic predictive model calibration for PMSMs with different topologies[J].Green Energy and Intelligent Transportation，2022，1(1) ：100015.

[3] DANESHZAND F, COKER P J, POTTER B, et al.EV smart charging: How tariff selection influences grid stress and carbon reduction[J].Applied Energy，2023，348 ：121482.

[4] 王惠文，孟潔. 多元線性回歸的預(yù)測(cè)建模方法[J]. 北京航空航天大學(xué)學(xué)報(bào)，2007，33(4) ：500-504.

[5] 彭丁聰. 卡爾曼濾波的基本原理及應(yīng)用[J]. 軟件導(dǎo)刊，2009，8(11) ：32-34.

[6] 包研科，陳然，鄭宏杰，等. 指數(shù)平滑與自回歸融合預(yù)測(cè)模型及實(shí)證[J] . 計(jì)算機(jī)工程與應(yīng)用，2022，58(14) ：269-281.

[7] SHI Jinkai, ZHANG Weige, BAO Yan, et al.Load forecasting of electric vehicle charging stations: Attention based spatiotemporal multigraph convolutional networks[J].IEEE Transactions on Smart Grid，2024，15(3) ：3016-3027.

[8] 尤勇，孟云龍，吳景濤，等. 基于鯨魚(yú)優(yōu)化算法-支持向量回歸的汽車運(yùn)動(dòng)狀態(tài)估計(jì)[J] . 中國(guó)機(jī)械工程，2024，35(6) ：973-981.

[9] 周澤楷，侯宏娟，孫莉，等. 基于 CNN 和 BiLSTM 神經(jīng)網(wǎng)絡(luò)模型的太陽(yáng)能供暖負(fù)荷預(yù)測(cè)研究[J] . 太陽(yáng)能學(xué)報(bào)，2024，45(10) ：415-422.

[10] 王義國(guó)，林峰，李琦，等. 基于 TCN-LSTM 模型的電網(wǎng)電能質(zhì)量擾動(dòng)分類研究[J] . 電力系統(tǒng)保護(hù)與控制，2024，52(17) ：161-167.

[11] 劉寧，謝越棟，胡彬，等. CNN-LSTM 車輛運(yùn)動(dòng)狀態(tài)識(shí)別的 AUKF 組合導(dǎo)航方法[J] . 中國(guó)慣性技術(shù)學(xué)報(bào)，2024，32(8) ：803-811.

[12] WANG Ning, GUO Jiahui, LIU Xiang, et al.A service demand forecasting model for oneway electric car-sharing systems combining long short-term memory networks with Granger causality test[J].Journal of Cleaner Production，2020，244 ：118812.

[13] ALI A, ZHU Y, ZAKARYA M.Exploiting dynamic spatiotemporal graph convolutional neural networks for citywide traffic flows prediction[J].Neural Networks， 2022，145 ：233-247.

[14] TIAN Jiarui, LIU Hui, GAN Wei, et al.Shortterm electric vehicle charging load forecasting based on TCN-LSTM network with comprehensive similar day identification[J].Applied Energy，2025，381 ：125174.

返回索引列表

SUBSCRIPTION MANAGEMENT

發(fā)行征訂

基于電動(dòng)汽車充電負(fù)荷變動(dòng)速率與TCN-LSTM的負(fù)荷預(yù)測(cè)