A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network
文献类型：期刊论文
| 作者 | Zhang, Tianren2,3; Huang, Yuping2,3,4,5,6; Liao, Hui3,4,5; Liang, Yu1 |
| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 351页码:16 |
| 关键词 | Electric vehicle Charge and discharge load classification Load forecasting Gradient boosting decision tree Temporal convolutional network |
| ISSN号 | 0306-2619 |
| DOI | 10.1016/j.apenergy.2023.121768 |
| 通讯作者 | Huang, Yuping(huangyp@ms.giec.ac.cn) |
| 英文摘要 | Due to the participation of large-scale electric vehicles (EVs) in Vehicle-to-Grid (V2G) services, V2G dispatch centers need to predict the charging and discharging (C&D) loads of electric vehicles in a short time period. This study proposes a novel machine learning based approach for EV load forecasting in power supply systems facing high resource uncertainty. This method takes advantage of both Gradient Boosting Decision Tree (GBDT) algorithm and Time Convolutional Network (TCN) model. This study considers the service decisions of EV users and uses the GBDT algorithm to classify the EV discharge load dataset, with 92% accuracy. Also, the TCN model is used to capture local temporal features and predict the future C&D loads. In comparison with other baseline models, e.g. CNN-BILSTM, LSTM, PSO-BP, the stability of the TCN model is superior in real data charging load forecasting, and it is shown that the TCN model has the smallest error. The feasibility of the proposed GBDT-TCN hybrid model is verified by numerical cases,and achieves the RMSE of discharging forecasting less than 6.23%. |
| WOS关键词 | CHARGING LOAD ; DEMAND ; MODEL |
| 资助项目 | National Key Research and Devel-opment Project[2022YFB3304500] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515012372] ; Science and Technology Program of Zhejiang Province, China[2022C03168] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001076002900001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Key Research and Devel-opment Project ; Guangdong Basic and Applied Basic Research Foundation ; Science and Technology Program of Zhejiang Province, China |
| 源URL | [http://ir.giec.ac.cn/handle/344007/39860]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Huang, Yuping |
| 作者单位 | 1.Sun Yat Sen Univ, Guangzhou 510275, Peoples R China 2.Univ Sci & Technol China, Sch Energy Sci & Engn, Hefei 230026, Peoples R China 3.Guangzhou Inst Energy Convers, Chinese Acad Sci, Guangzhou 510640, Peoples R China 4.Chinese Acad Sci, Key Lab Renewable Energy, Guangzhou 510640, Peoples R China 5.Guangdong Prov Key Lab New & Renewable Energy Res, Guangzhou 510640, Peoples R China 6.Univ Sci & Technol China, Sch Energy Sci & Engn, USTC, Hefei 230026, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Tianren,Huang, Yuping,Liao, Hui,et al. A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network[J]. APPLIED ENERGY,2023,351:16. |
| APA | Zhang, Tianren,Huang, Yuping,Liao, Hui,&Liang, Yu.(2023).A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network.APPLIED ENERGY,351,16. |
| MLA | Zhang, Tianren,et al."A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network".APPLIED ENERGY 351(2023):16. |
入库方式： OAI收割
来源：广州能源研究所
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