Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR
The accurate short-term forecasting of an electric vehicle (EV) load is crucial for the reliable operation of a power grid and for effectively reducing energy consumption. Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial...
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MDPI AG
2024-12-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/12/582 |
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| author | Renxue Shang Yongjun Ma |
| author_facet | Renxue Shang Yongjun Ma |
| author_sort | Renxue Shang |
| collection | DOAJ |
| description | The accurate short-term forecasting of an electric vehicle (EV) load is crucial for the reliable operation of a power grid and for effectively reducing energy consumption. Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial areas, global models often suffer from prediction errors when forecasting loads. To address this issue, this paper proposes a regional forecasting method based on K-means++ clustering and deep learning algorithms. First, the K-means++ algorithm was used to partition the data into different regions, and an independent load-forecasting model was established for each region. Then, a combination of kernel support vector regression (KSVR) and gated recurrent unit (GRU) models was used to handle nonlinear features and time-dependent data, where particle swarm optimization (PSO) further optimized the model parameters to improve the forecasting accuracy. Finally, a weighted summation method was used to integrate the forecast results from each region, resulting in a more accurate overall load forecast. The experimental results show that the proposed model provided better prediction performance by capturing the spatiotemporal characteristics of the EV charging load, effectively addressing the challenges posed by regional differences, and outperforming the single-model forecasts. |
| format | Article |
| id | doaj-art-f0bca1654e03417b8a29f8a2787b3dbf |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-f0bca1654e03417b8a29f8a2787b3dbf2025-08-20T02:43:50ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-12-01151258210.3390/wevj15120582Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVRRenxue Shang0Yongjun Ma1School of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300450, ChinaSchool of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300450, ChinaThe accurate short-term forecasting of an electric vehicle (EV) load is crucial for the reliable operation of a power grid and for effectively reducing energy consumption. Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial areas, global models often suffer from prediction errors when forecasting loads. To address this issue, this paper proposes a regional forecasting method based on K-means++ clustering and deep learning algorithms. First, the K-means++ algorithm was used to partition the data into different regions, and an independent load-forecasting model was established for each region. Then, a combination of kernel support vector regression (KSVR) and gated recurrent unit (GRU) models was used to handle nonlinear features and time-dependent data, where particle swarm optimization (PSO) further optimized the model parameters to improve the forecasting accuracy. Finally, a weighted summation method was used to integrate the forecast results from each region, resulting in a more accurate overall load forecast. The experimental results show that the proposed model provided better prediction performance by capturing the spatiotemporal characteristics of the EV charging load, effectively addressing the challenges posed by regional differences, and outperforming the single-model forecasts.https://www.mdpi.com/2032-6653/15/12/582electric vehicle power loadK-means++gated recurrent unitkernel support vector regression |
| spellingShingle | Renxue Shang Yongjun Ma Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR World Electric Vehicle Journal electric vehicle power load K-means++ gated recurrent unit kernel support vector regression |
| title | Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR |
| title_full | Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR |
| title_fullStr | Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR |
| title_full_unstemmed | Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR |
| title_short | Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR |
| title_sort | electric vehicle charging load forecasting based on k means gru ksvr |
| topic | electric vehicle power load K-means++ gated recurrent unit kernel support vector regression |
| url | https://www.mdpi.com/2032-6653/15/12/582 |
| work_keys_str_mv | AT renxueshang electricvehiclechargingloadforecastingbasedonkmeansgruksvr AT yongjunma electricvehiclechargingloadforecastingbasedonkmeansgruksvr |