Open Access
| Issue |
Sci. Tech. Energ. Transition
Volume 78, 2023
|
|
|---|---|---|
| Article Number | 15 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.2516/stet/2023011 | |
| Published online | 12 July 2023 | |
- Comello S., Reichelstein S., Sahoo A. (2018) The road ahead for solar PV power, Renewable Sustainable Energy Rev. 92, 744–756. [CrossRef] [Google Scholar]
- Lin P., Peng Z., Lai Y., Cheng S., Chen Z., Wu L. (2018) Short-term power prediction for photovoltaic power plants using a hybrid improved Kmeans-GRA-Elman model based on multivariate meteorological factors and historical power datasets, Energy Convers. Manage. 177, 704–717. [CrossRef] [Google Scholar]
- Li J., Luo Y., Yang S., Wei S., Huang Q. (2021) Review of renewable energy power uncertainty prediction methods, High Volt. Technol. 47, 4, 1144–1157. [Google Scholar]
- Sobri S., Koohi-Kamali S., Abd Rahim N. (2018) Solar photovoltaic generation forecasting methods: A review, Energy Convers. Manage. 156, 459–497. [CrossRef] [Google Scholar]
- Yao X., Mao S. (2023) Electric supply and demand forecasting using seasonal grey model based on PSO-SVR, Grey Syst. Theory Appl. 13, 1, 141–171. [CrossRef] [Google Scholar]
- Yu N., Li X., Fei K., Ren J., Ni X. (2020) Based on SVR – UKF photovoltaic power station power prediction, Autom. Instrum. 246, 4, 73–77. [Google Scholar]
- Wang X., Liu J., Hu B., Zheng L. (2020) Based on CS – SVR model of short-term wind power prediction, Comput. Meas. Control. 28, 1, 152–155. [Google Scholar]
- Li M., Gu Y., Zhang Y., Gao X., Wei G. (2023) Quantitative prediction of ternary mixed gases based on an SnO2 sensor array and an SSA-BP neural net-work model, Phys. Chem. Chem. Phys. 25, 10935–10945. [CrossRef] [PubMed] [Google Scholar]
- Li N., Wang Y., Ma W., Xiao Z., An Z. (2022) A wind power prediction method based on DE-BP neural network, Front. Energy Res. 10, 15–25. [Google Scholar]
- Liu W., Guo Z.-Q., Wang D., Liu G.-W., Jiang F., Niu Y.-J., Ma L.-X. (2023) Whales algorithm and its weights in the shallow layer neural network search threshold optimization, J. Control Decis. 38, 4, 1144–1152. [Google Scholar]
- Zhang Y., Cheng Q., Jiang W., Liu X., Shen L., Chen Z.H. (2021) Photovoltaic power prediction model based on EMD-PCA-LSTM, Acta Energiae Solaris Sinica 42, 9, 62–69. [Google Scholar]
- Zhang L., Wang X., Wu H., Xie L., Teng Y., Wei Y. (2023) Based on FCM and LSTM photovoltaic power short-term prediction, Power Supply 40, 1, 10–17. [Google Scholar]
- Huang Y., Zhang X., Yang L. (2019) Short-term wind speed prediction based on EEMD-LSTM, J. Phys. Conf. Ser. 1314, 012105. [Google Scholar]
- Liu D., Zhou L., Zheng X. (2021) Super short-term power load forecasting based on SA - DBN, J. Guangxi Normal University (Natural Science Edition) 33, 4, 21–33. [Google Scholar]
- Zhang C., He Y., Jiang S., Wang T., Yuan L., Li B. (2019) Transformer fault diagnosis method based on self-powered RFID sensor tag, DBN, and MKSVM, IEEE Sens. J. 19, 18. [Google Scholar]
- Garai S., Paul R.K. (2023) Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence, Intell. Syst. Appl. 18, 200202. [Google Scholar]
- He Y., Zhou C., Hu Y. (2023) Application of LSTM method combined with feature optimization in chiller failure detection, J. Phys. Conf. Ser. 2442, 012026. [Google Scholar]
- Wang J., Hao S., Li S., Wang T.-Z., Zhang W. Prediction of wind farm group power based on ES-GRU-LSTM, Comput. Technol. Automat. 202, 37–41 (in Chinese). [Google Scholar]
- Zeng W., Cao Y., Feng L., Fan J., Zhong M., Mo W., Tan Z. (2023) Hybrid CEEMDAN-DBN-ELM for online DGA serials and transformer status forecasting, Electr. Power Syst. Res. 217, 109176. [CrossRef] [Google Scholar]
- Xue J. (2020) Research and application of a new swarm intelligence optimization technique, Donghua University. [Google Scholar]
- Jiankai X., Bo S. (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Syst. Sci. Control Eng. 8, 1, 22–34. [CrossRef] [Google Scholar]
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