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Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation
Open accessAbstract
Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.
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“This algorithm has proven successful in several diagnostic applications (Behravan et al., 2018; Torlay et al., 2017; Zheng et al., 2017).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Zheng et al., 2017
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Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2018) · Energies · doi:10.3390/en11071636 · Cited by 941
Himeur, Y., Elnour, M., Fadli, F., Meskin, N., Petri, I., Rezgui, Y., Bensaali, F., & Amira, A. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10286-2 · Cited by 630
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Metadata
- Authors
- Zheng, H., Yuan, J., & Chen, L.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- Corresponding author
- Zheng, Huiting
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2017-08-08
- Volume
- 10
- Issue
- 8
- Pages
- 1168
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2743680082
- Cited by
- 635
- References
- 35
- Citations by year
- 2026: 31 · 2025: 54 · 2024: 77 · 2023: 83 · 2022: 112 · 2021: 111 · 2020: 74 · 2019: 64 · 2018: 25 · 2017: 4
- FWCI
- 18.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Key Research and Development Program of China (No. 2017YFB0802303)
- Application platform and Industrialization for efficient cloud computing for Big data of the Science and Technology Supported Program of Jiangsu Province (BA2015052)
- Funding of National Natural Science Foundation of China (Grant No.61571226)
- Jiangsu Program for the transformation of scientific and technological achievements (BA2015051)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- short-term load forecasting, empirical mode decomposition, long short-term memory, similar day selection, XGBoost, feature importance, intrinsic mode functions, hybrid forecasting model, power system operation
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