The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security
Open accessAbstract
As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply–demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on variational mode decomposition and long short-term memory with seasonal factors elimination and error correction is proposed in this article. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models.
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Cites
Variational Mode Decomposition
Dragomiretskiy, K., & Zosso, D. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2288675 · Cited by 9,184
Industry 5.0: A survey on enabling technologies and potential applications
Maddikunta, P. K. R., Pham, Q.-V., B, P., Deepa, N., Dev, K., Gadekallu, T. R., Ruby, R., & Liyanage, M. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100257 · Cited by 1,890
Xia, M., Shao, H., Ma, X., & de Silva, C. W. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3056867 · Cited by 414
A hybrid model for building energy consumption forecasting using long short term memory networks
Somu, N., M R, G. R., & Ramamritham, K. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114131 · Cited by 396
He, F., Zhou, J., Feng, Z.-K., Liu, G., & Yang, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.055 · Cited by 342
A combined forecasting model for time series: Application to short-term wind speed forecasting
Liu, Z., Jiang, P., Zhang, L., & Niu, X. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114137 · Cited by 307
Self-citationZhang, Y., Chen, B., Pan, G., & Zhao, Y. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.05.005 · Cited by 276
Liang, Y., Niu, D., & Hong, W.-C. (2019) · Energy · doi:10.1016/j.energy.2018.10.119 · Cited by 269
A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid
Alazab, M., Khan, S., Krishnan, S. S. R., Pham, Q.-V., Reddy, M. P. K., & Gadekallu, T. R. (2020) · IEEE Access · doi:10.1109/access.2020.2991067 · Cited by 259
Jiang, X., Wang, J., Shi, J., Shen, C., Huang, W., & Zhu, Z. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.07.014 · Cited by 234
Cited by
An edge-ai based forecasting approach for improving smart microgrid efficiency
Lv, L., Wu, Z., Zhang, L., Gupta, B. B., & Tian, Z. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3163137 · Cited by 155
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Metadata
- Authors
- Lv, L., Wu, Z., Zhang, J., Zhang, L., Tan, Z., & Tian, Z.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-09
- Volume
- 18
- Issue
- 9
- Pages
- 6474-6482
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3215693540
- Cited by
- 282
- References
- 19
- FWCI
- 12.17
- Type
- journal article
- Language
- en
- Open access
- green
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