Research on Multi-energy Coupled Power Load Prediction Based on K-means Clustering and LSTM Model

Yaming GE; Chenguang QIU; Lirong XIE; Yifeng LI; Gang LI; Yulin ZHAO

doi:10.19725/j.cnki.1007-2322.2023.0110

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Research on Multi-energy Coupled Power Load Prediction Based on K-means Clustering and LSTM Model

Electricity Market | 更新时间：2025-10-13

- Research on Multi-energy Coupled Power Load Prediction Based on K-means Clustering and LSTM Model
- “Under the goal of "peak carbon emissions and carbon neutrality", experts analyze the energy consumption characteristics of multi energy coupling and use LSTM models to predict power loads, providing a theoretical basis for multi energy coupling power load forecasting.”
- Modern Electric Power Vol. 42, Issue 2, Pages: 369-376(2025)
- 作者机构：
  
  1.国网江苏省电力有限公司, 江苏省南京市 211102
  2.国电南瑞南京控制系统有限公司, 江苏省南京市 211100
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0110
  CLC： TM72
- Received：28 March 2023，
  
  Accepted：05 December 2023，
  
  Published：10 April 2025
- 稿件说明：
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Yaming GE, Chenguang QIU, Lirong XIE, et al. Research on Multi-energy Coupled Power Load Prediction Based on K-means Clustering and LSTM Model[J]. Modern electric power, 2025, 42(2): 369-376.
DOI：

Yaming GE, Chenguang QIU, Lirong XIE, et al. Research on Multi-energy Coupled Power Load Prediction Based on K-means Clustering and LSTM Model[J]. Modern electric power, 2025, 42(2): 369-376. DOI： 10.19725/j.cnki.1007-2322.2023.0110.

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Research on Multi-energy Coupled Power Load Prediction Based on K-means Clustering and LSTM Model

DOI：10.19725/j.cnki.1007-2322.2023.0110