GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings
Abstract
:1. Introduction
- (1)
- An innovative data-driven GCN-Transformer model is proposed, which is particularly effective in managing multitask forecasting challenges arising from the wide distribution of multiple EVBSSs. It is designed to analyze complex scenarios involving a broad spectrum of multinode loads with significant interdependencies. Unlike traditional Transformer methods, which often inadequately account for mutual influences among loads in multinode forecasting scenarios, the GCN-Transformer model seamlessly integrates these considerations, offering a more sophisticated and comprehensive solution.
- (2)
- The graph network structure is further refined using a novel Spearman’s rank correlation coefficient, which integrates multidimensional node features by optimally selecting influential factors. In contrast to traditional graph convolutional neural networks, which focus solely on node information and are constrained by the inherent attributes of the data, the incorporation of the Transformer equips the model with a multitude of reference factors, thereby enhancing its performance in handling time series problems.
2. Influence Factor Analysis
2.1. Historical Load Data
2.2. Regional Factors
2.3. Meteorological Factors
2.4. Date Factors
3. GCN-Transformer Multiswap Station Load Joint Forecasting Model
3.1. GCN Model
3.2. Transformer Model
3.3. GCN-Transformer Model Evaluation Index
4. Case Study
4.1. Experimental Environment
4.2. Experimental Datasets
4.3. Experimental Results and Analysis
4.4. GCN-Transformer Effectiveness Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Almaghrebi, A.; James, K.; Al Juheshi, F.; Alahmad, M. Insights into household electric vehicle charging behavior: Analysis and predictive modeling. Energies 2024, 17, 925. [Google Scholar] [CrossRef]
- Bampos, Z.N.; Laitsos, V.M.; Afentoulis, K.D.; Vagropoulos, S.I.; Biskas, P.N. Electric vehicles load forecasting for day-ahead market participation using machine and deep learning methods. Appl. Energy 2024, 360, 122801. [Google Scholar] [CrossRef]
- Chan, J.W.; Yeo, C.K. Electrical power consumption forecasting with transformers. In Proceedings of the 2022 IEEE Electrical Power and Energy Conference (EPEC), Victoria, BC, Canada, 5–7 December 2022; pp. 255–260. [Google Scholar] [CrossRef]
- Dai, Q.; Huo, X.; Hao, Y.; Yu, R. Spatio-temporal prediction for distributed PV generation system based on deep learning neural network model. Front. Energy Res. 2023, 11, 1204032. [Google Scholar] [CrossRef]
- Gnanavendan, S.; Selvaraj, S.K.; Dev, S.J.; Mahato, K.K.; Swathish, R.S.; Sundaramali, G.; Accouche, O.; Azab, M. Challenges, solutions and future trends in EV-technology: A review. IEEE Access 2024, 12, 17242–17260. [Google Scholar] [CrossRef]
- Gupta, A.K.; Singh, R.K. Short-term day-ahead photovoltaic output forecasting using PCA-SFLA-GRNN algorithm. Front. Energy Res. 2022, 10, 1029449. [Google Scholar] [CrossRef]
- He, L.; Li, L.; Li, M.; Li, Z.; Wang, X. A deep learning approach to the transformer life prediction considering diverse aging factors. Front. Energy Res. 2022, 10, 930093. [Google Scholar] [CrossRef]
- Huang, N.; He, Q.; Qi, J.; Hu, Q.; Wang, R.; Cai, G.; Yang, D. Multinodes interval electric vehicle day-ahead charging load forecasting based on joint adversarial generation. Int. J. Electr. Power Energy Syst. 2022, 143, 108404. [Google Scholar] [CrossRef]
- Kumar, R.R.; Bharatiraja, C.; Udhayakumar, K.; Devakirubakaran, S.; Sekar, K.S.; Mihet-Popa, L. Advances in batteries, battery modeling, battery management system, battery thermal management, SOC, SOH, and charge/discharge characteristics in EV applications. IEEE Access 2023, 11, 105761–105809. [Google Scholar] [CrossRef]
- Li, S.; Hu, W.; Cao, D.; Dragicevic, T.; Huang, Q.; Chen, Z.; Blaabjerg, F. Electric vehicle charging management based on deep reinforcement learning. J. Mod. Power Syst. Clean Energy 2022, 10, 719–730. [Google Scholar] [CrossRef]
- Li, S.; Zhao, P.; Gu, C.; Li, J.; Cheng, S.; Xu, M. Battery protective electric vehicle charging management in renewable energy system. IEEE Trans. Ind. Inform. 2023, 19, 1312–1321. [Google Scholar] [CrossRef]
- Li, Y.; Wei, Y.; Zhu, F.; Du, J.; Zhao, Z.; Ouyang, M. The path enabling storage of renewable energy toward carbon neutralization in China. eTransportation 2023, 16, 100226. [Google Scholar] [CrossRef]
- Liang, Y.; Ding, Z.; Zhao, T.; Lee, W.-J. Real-time operation management for battery swapping-charging system via multi-agent deep reinforcement learning. IEEE Trans. Smart Grid 2023, 14, 559–571. [Google Scholar] [CrossRef]
- Liu, K.; Li, K.; Peng, Q.; Zhang, C. A brief review on key technologies in the battery management system of electric vehicles. Front. Mech. Eng. 2019, 14, 47–64. [Google Scholar] [CrossRef]
- Song, H.; Al Khafaf, N.; Kamoona, A.; Sajjadi, S.S.; Amani, A.M.; Jalili, M.; Yu, X.; McTaggart, P. Multitasking recurrent neural network for photovoltaic power generation prediction. Energy Rep. 2023, 9, 369–376. [Google Scholar] [CrossRef]
- Sun, X.; Fu, J.; Yang, H.; Xie, M.; Liu, J. An energy management strategy for plug-in hybrid electric vehicles based on deep learning and improved model predictive control. Energy 2023, 269, 126772. [Google Scholar] [CrossRef]
- Wang, Q.; Wang, Z.; Zhang, L.; Liu, P.; Zhou, L. A battery capacity estimation framework combining hybrid deep neural network and regional capacity calculation based on real-world operating data. IEEE Trans. Ind. Electron. 2023, 70, 8499–8508. [Google Scholar] [CrossRef]
- Xing, Y.; Li, F.; Sun, K.; Wang, D.; Chen, T.; Zhang, Z. Multi-type electric vehicle load prediction based on Monte Carlo simulation. Energy Rep. 2022, 8, 966–972. [Google Scholar] [CrossRef]
- Zahoor, A.; Mehr, F.; Mao, G.; Yu, Y.; Sápi, A. The carbon neutrality feasibility of worldwide and in China’s transportation sector by E-car and renewable energy sources before 2060. J. Energy Storage 2023, 61, 106696. [Google Scholar] [CrossRef]
- Zhong, B. Deep learning integration optimization of electric energy load forecasting and market price based on the ANN–LSTM–transformer method. Front. Energy Res. 2023, 11, 1292204. [Google Scholar] [CrossRef]
Item | Parameter |
---|---|
Activation function | ReLu |
Optimizer | Adam |
Maximum epoch | 1500 |
Learning rate | 0.001 |
Batch size | 64 |
Load | Evaluation Index (%) | Proposed Model | TCN-LSTM | CNN-LSTM |
---|---|---|---|---|
Battery swapping station load A | 1.04 | 3.36 | 4.66 | |
98.96 | 96.64 | 95.34 | ||
Battery swapping station load B | 3.23 | 5.24 | 6.78 | |
97.77 | 94.76 | 93.22 | ||
Battery swapping station load C | 8.42 | 11.95 | 13.75 | |
98.58 | 88.05 | 86.25 |
Neural Network | Feature Dimension | Training Time (s) |
---|---|---|
RNN | 1 | 998.7 |
TCN-LSTM | 7 | 3294.0 |
CNN-LSTM | 7 | 2677.13 |
Transformer | 1 | 2988.07 |
Proposed model | 8 | 3372.46 |
Load | Evaluation Index (%) | Transformer | Proposed Model |
---|---|---|---|
Battery swapping station load | 6.54% | 4.84% | |
93.66% | 95.16% |
Load | Evaluation Index (%) | RNN | TCN-LSTM |
Battery swapping station load | 12.02% | 9.67% | |
87.98% | 90.33% | ||
Load | Evaluation Index (%) | CNN-LSTM | Proposed Model |
Battery swapping station load | 7.23% | 4.73% | |
92.77% | 95.27% |
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Hu, X.; Zhang, Z.; Fan, Z.; Yang, J.; Yang, J.; Li, S.; He, X. GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings. Electronics 2024, 13, 3401. https://doi.org/10.3390/electronics13173401
Hu X, Zhang Z, Fan Z, Yang J, Yang J, Li S, He X. GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings. Electronics. 2024; 13(17):3401. https://doi.org/10.3390/electronics13173401
Chicago/Turabian StyleHu, Xiao, Zezhen Zhang, Zhiyu Fan, Jinduo Yang, Jiaquan Yang, Shaolun Li, and Xuehao He. 2024. "GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings" Electronics 13, no. 17: 3401. https://doi.org/10.3390/electronics13173401