Open Access
| Issue |
E3S Web Conf.
Volume 360, 2022
2022 8th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2022)
|
|
|---|---|---|
| Article Number | 01079 | |
| Number of page(s) | 19 | |
| DOI | https://doi.org/10.1051/e3sconf/202236001079 | |
| Published online | 23 November 2022 | |
- Y.-F. Li, Electricity load forecasting. [J] Journal of Nanchang College, 2007, 22(6): 143–145. [Google Scholar]
- J. Huo, X.-W. Sun, M.-J. Zhang, Comparison Between Power Load Forecasting Algorithms Based on Random Forest and Support Vector Machine. [J] Proceedings of the CSU-EPSA, 2019, 31(07):129–134. DOI:10.19635/j.cnki.csu-epsa.000093. [Google Scholar]
- S.-H. Guan, Y.-X. Shen, Power load forecasting based on PSO RBF-NN. [J] Transducer and Microsystem Technologies, 2021, 40(05):128–131. DOI:10.13873/J.1000-9787 (2021)05-0128-04. [Google Scholar]
- Y.-J. Dong, X.-T. Wang, H.-M. Ma, L.-B. Wang, M.-Y. Li, F.-D. Yue, H. Yuan, Power Load Forecasting Method Based on Random Forest and Long Short-term Memory [J] Journal of Global Energy Interconnection, 2022, 5(02):147–156. DOI:10.19705/j.cnki.issn2096-5125.2022.02.006. [Google Scholar]
- Bozkurt Ö.Ö., Biricik G., Tayşi Z.C. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLoS One 2017; 12(4): e0175915. doi: DOI: 10.1371/journal.pone.0175915. [CrossRef] [PubMed] [Google Scholar]
- Neeraj, Mathew, J. & Behera, R.K. Power load forecasting based on long short term memory-singular spectrum analysis. Energy Syst (2021). https://doi.org/10.1007/s12667-020-00424-6 [Google Scholar]
- J.-F. Zhu, W.-G. Chen, H.-T. Zai, K. Zhang, X.-R. Wang, Comparative discussion on power load forecasting based on ARIMA and LSTM. [J] Theoretical Analysis, 2022, 41(02):27–31. [Google Scholar]
- B. Zhang, F. Zhou, Q. Li, Traffic Delay Index Forecast of Beijing Capital International Airport Based on LSTM Model. [J] Journal of Applied Statistics and Management, 2020, 39(05):761–770. DOI:10.13860/j.cnki.sltj.20200818-002. [Google Scholar]
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