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NGO-CNN-LSTM Power Load Short-term Combination Forecasting Model Based on ALIF-VMD Quadratic Decomposition
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Lin ZHANG1, Sheng-qiang GAO1, *, Yu SONG2, Shuai-yu BU1, Wei YU1
Science Technology and Engineering | 2025, 25(11) : 4583 - 4597
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Science Technology and Engineering | 2025, 25(11): 4583-4597
• Papers·Electrical Technology •
NGO-CNN-LSTM Power Load Short-term Combination Forecasting Model Based on ALIF-VMD Quadratic Decomposition
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Lin ZHANG1, Sheng-qiang GAO1, *, Yu SONG2, Shuai-yu BU1, Wei YU1
Affiliations
  • 1 State Grid Beijing Electric Power Company, Beijing 100031, China
  • 2 The College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
Published: 2025-04-18 doi: 10.12404/j.issn.1671-1815.2404503
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Aiming at obvious load fluctuation trend, strong randomness and low accuracy caused by unreasonable parameter values of the prediction model involved into the power load forecasting process, a combined prediction model composing of ALIF (adaptive local iterative filtering), VMD (variational mode decomposition), NGO (northern goshawk optimization) and CNN-LSTM (convolutional neural networks - long short-term memory) was established. Firstly, CCM (convergent cross-mapping) method was used to identify the key factors affecting the power load. Secondly, an innovative combination of ALIF, NGO-based VMD and FE (fuzzy entropy) was employed for combinatorial decomposition and necessary recombination of original load sequence. Next, based on the modal components generated after decomposition and recombination, combined with optimal hyperparameter combination of CNN-LSTM determined by NGO method, an NGO-CNN-LSTM day-ahead power load combination prediction model with the high prediction accuracy, short training time and fast convergence speed was formulated. Compared with other benchmark models, the obtained results demonstrated that the proposed model has the better adaptability and prediction accuracy, and can provide important technical support for the safe, reliable and economical operation of power system.

load forecasting  /  sequence decomposition and recombination  /  northern goshawk optimization  /  convolutional neural network-long short-term memory neural network model
Lin ZHANG, Sheng-qiang GAO, Yu SONG, Shuai-yu BU, Wei YU. NGO-CNN-LSTM Power Load Short-term Combination Forecasting Model Based on ALIF-VMD Quadratic Decomposition[J]. Science Technology and Engineering, 2025 , 25 (11) : 4583 -4597 . DOI: 10.12404/j.issn.1671-1815.2404503
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Article Info
doi: 10.12404/j.issn.1671-1815.2404503
  • Receive Date：2024-06-17
  • Online Date：2025-07-09
  • Published：2025-04-18
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  • Received：2024-06-17
  • Revised：2024-10-29
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    1 State Grid Beijing Electric Power Company, Beijing 100031, China
    2 The College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
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