Cited by 638Energy Load and Power ForecastingStock Market Forecasting MethodsTime Series Analysis and Forecasting

Short-term load forecasting using EMD-LSTM neural networks with a XGBoost algorithm for feature importance evaluation

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Zheng, Huiting, Yuan, Jiabin, Chen, Long·2017·Energies

Abstract

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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  1. Mitchell, T.M. Machine Learning; McGraw Hill: Burr Ridge, IL, USA, 1997; Volume 45, pp. 230-247.
  2. Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J. Data Mining: Practical Machine Learning Tools and Techniques; Morgan Kaufmann:Burlington, MA, USA, 2016.

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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
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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