Multi-Step Ahead Short-Term Load Forecasting Based on MCNN-MMoL

Suxun Zhu; Bowen Ren; Lili Zheng; Abdullah M. Alharbi; Changhua Yang; Shidong Wu; Aotian Yuan; Hengrui Ma

doi:10.1049/ses2.70020

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Multi-Step Ahead Short-Term Load Forecasting Based on MCNN-MMoL

更新时间：2026-03-24

- Multi-Step Ahead Short-Term Load Forecasting Based on MCNN-MMoL
- IET Smart Energy Systems Vol. 2, Issue 1, (2026)
- 作者机构：
  
  1. School of Astronautics, Harbin Institute of Technology,Harbin,China
  2. Anhui Usem Technology Co. Ltd.,Wuhu,China
  3. School of Energy and Electrical Engineering, Qinghai University,Xining,China
  4. Contribution: Investigation, Validation, Visualization
  5. Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
  6. State Grid Zhejiang Electric Power Co. Ltd. Construction Branch Company,Hangzhou,China
  7. Institute of Science and Technology, China Three Gorges Corporation,Beijing,China
  8. School of Automation, Wuhan University of Technology,Wuhan,China
- 作者简介：
- 基金信息：
- DOI：10.1049/ses2.70020
  CLC：
- Published：2026
- 稿件说明：
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Suxun Zhu, Bowen Ren, Lili Zheng, et al. Multi-Step Ahead Short-Term Load Forecasting Based on MCNN-MMoL[J]. IET Smart Energy Systems, 2026, 2(1).
DOI：

Suxun Zhu, Bowen Ren, Lili Zheng, et al. Multi-Step Ahead Short-Term Load Forecasting Based on MCNN-MMoL[J]. IET Smart Energy Systems, 2026, 2(1). DOI： 10.1049/ses2.70020.

摘要

Abstract

Multi-step ahead forecasting of power load is crucial for optimising power system scheduling and participating in energy market transactions. However

existing forecasting methods often suffer from issues such as cumulative prediction errors and insufficient modelling of sequence dependencies. To solve the above problems

a multi-step ahead forecasting method based on multiplexed convolutional neural networks (MCNN) and multi-gate mixture of long short-term memory networks (MMoL). First

multi-branch convolution is adopted to construct independent feature spaces for different levels of power loads

achieving multi-scale feature fusion to enhance the representation ability of the input samples. Next

the multi-step prediction task is transformed into a multi-task joint optimisation problem. Multiple independent LSTMs are used as shared experts

and task-specific gating units are utilised to dynamically learn the optimal combination of expert models for each future time step

achieving more refined time-series feature modelling. Finally

comparative experiments are conducted based on two real-world datasets. The results show that the proposed model exhibits better accuracy and robustness.

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