The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network
Abstract
The short-term load forecasting is crucial in the power system operation and control. However, due to its nonstationary and complicated random features, an accurate forecast of the load behavior is challenging. An improved short-term load forecasting method is proposed in this article. At first, the load is decomposed into different frequency components varying from the low to high levels realized by the ensemble empirical-mode decomposition algorithm. Then, the smooth and periodic low-frequency components are predicted by the multivariable linear regression method while maintaining the efficient computation capacity, while the high-frequency components with strong randomness are forecasted by the long short-term memory neural network algorithms. Thus, the actual load behavior is obtained by combining these two methods. Finally, the proposed method is validated by experiments, in which the tested data from the west area of China, Uzbekistan, and PJM Interconnection (USA) are used. The prediction of the load behavior is accurate globally along with the local details, as presented in the experiments, which verify the effectiveness of the proposed method.
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Cites
Adam: A Method for Stochastic Optimization
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ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
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Neural networks for short-term load forecasting: a review and evaluation
Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001) · IEEE Transactions on Power Systems · doi:10.1109/59.910780 · Cited by 2,221
Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001
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Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
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A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods
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Cited by
Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237
Hossain Lipu, M. S., Rahman, M. S. A., Mansor, M., Rahman, T., Ansari, S., Fuad, A. M., & Hannan, M. A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113172 · Cited by 41
Metadata
- Authors
- Li, J., Deng, D., Zhao, J., Cai, D., Hu, W., Zhang, M., & Huang, Q.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-04
- Volume
- 17
- Issue
- 4
- Pages
- 2443-2452
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3033406500
- Cited by
- 269
- References
- 21
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, J., Deng, D., Zhao, J., Cai, D., Hu, W., Zhang, M., & Huang, Q. (2021). A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2020.3000184
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