Home Journals Articles Updates About
CN
image
收藏切换
Short-term Power Load Forecasting Based on CVMD-GRU-DenseNet Hybrid Model
收藏切换
PDF
Ke ZHANG1, Dan LI1, Guang-fan SUN1, 2, Ya TAN1, 2, Shuai HE1, 2
Water Resources and Power | 2023, 41(1) : 207 - 211
Less
收藏切换
Water Resources and Power | 2023, 41(1): 207-211
• ELECTRICAL ENGINEERING •
Short-term Power Load Forecasting Based on CVMD-GRU-DenseNet Hybrid Model
Full
Ke ZHANG1, Dan LI1, Guang-fan SUN1, 2, Ya TAN1, 2, Shuai HE1, 2
Affiliations
  • 1.College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
  • 2.Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Published: 2023-01-25 doi: 10.20040/j.cnki.1000-7709.2023.20221985
Outline
收藏切换

A CVMD-GRU-DenseNet model for short-term load forecasting based on a decomposition-prediction-reconstruction framework is proposed to aim at the nonlinear and multi-period characteristics of power load time series. In the decomposition stage, the optimal decomposition number of VMD is determined according to the correlation entropy between subsequences to improve the decomposition quality. In the prediction stage, the input features are selected according to the characteristics of each sub-sequence. The GRU neural network and the DenseNet model are employed to forecast the regular low-frequency and highly random high-frequency components, respectively. Finally, the prediction results for each element are reconstructed into a load prediction curve. The short-term load forecasting results of four seasons for a city in Hubei Province show that the proposed method can effectively improve forecasting accuracy and has strong generalization ability.

short-term load forecasting  /  variational modal decomposition  /  conrrentropy  /  gated recurrent units  /  densely connected convolution networks
Ke ZHANG, Dan LI, Guang-fan SUN, Ya TAN, Shuai HE. Short-term Power Load Forecasting Based on CVMD-GRU-DenseNet Hybrid Model[J]. Water Resources and Power, 2023 , 41 (1) : 207 -211 . DOI: 10.20040/j.cnki.1000-7709.2023.20221985
Appendix
Less
Year 2023 volume 41 Issue 1
PDF
93
13
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221985
  • Receive Date：2022-09-23
  • Online Date：2026-01-28
  • Published：2023-01-25
Article Data
Affiliations
History
  • Received：2022-09-23
  • Revised：2022-10-17
Funding
Affiliations
    1.College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    2.Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20221985
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House