基于多任务学习的冷-热-电负荷联合预测研究

王东风; 贾若彤; 李朋然; 齐帅任; 谢家乐; 黄宇

doi:10.19912/j.0254-0096.tynxb.2025-0671

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基于多任务学习的冷-热-电负荷联合预测研究

更新时间：2026-10-15

- 基于多任务学习的冷-热-电负荷联合预测研究
- Vol. 47, Issue 9, Pages: 505-515(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0671
  CLC： TM715
- Online First：10 October 2026，
  
  Published：2026
- 稿件说明：
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王东风, 贾若彤, 李朋然, et al. 基于多任务学习的冷-热-电负荷联合预测研究[J]. 2026, 47(9): 505-515.
DOI：

王东风, 贾若彤, 李朋然, et al. 基于多任务学习的冷-热-电负荷联合预测研究[J]. 2026, 47(9): 505-515. DOI： 10.19912/j.0254-0096.tynxb.2025-0671.

摘要

针对单一预测模型忽略跨负荷关联特征导致预测效果较差的问题

提出一种基于多任务学习的组合模型对多元负荷进行预测

以提高预测的准确性。首先

量化气象因素与负荷的相关性

确定模型的输入特征;其次

构建预测模型

采用卷积神经网络（CNN）提取输入特征与负荷之间的非线性空间关联

双向长短期记忆网络（BiLSTM）模块捕获负荷的长期依赖性和时间特征

并引入时间模式注意力（TPA）机制自适应赋权突出重要时间信息;最后

采用多任务学习提取冷、热、电负荷的公共特征

捕捉其耦合关系

在任务特定分支分别预测不同类型的负荷。通过公开数据集开展实验

结果表明

冷、热、电负荷联合预测相比单任务预测模型的平均绝对百分比误差（MAPE）分别降低36.30%、23.93%和22.63%。该方法不仅可提升负荷预测的准确性和稳定性

还能更好地反映综合能源系统中多能源耦合特性。

Abstract

Aiming to address the suboptimal performance of single prediction models

which often neglect cross-load correlation features

this study proposes a multi-task learning-based combined model for multi-load forecasting to enhance prediction accuracy. Firstly

the correlation between meteorological factors and loads was quantified to determine the model's input features. Secondly

a prediction model was constructed

utilizing a convolutional neural network （CNN） to extract nonlinear spatial correlations between input features and loads

a bidirectional long short-term memory （BiLSTM） module to capture the long-term dependencies and temporal features of the loads

and a temporal pattern attention （TPA） mechanism to adaptively weight and highlight important temporal information. Finally

multi-task learning （MTL） was employed to extract shared features among cooling

heating

and electricity loads

capturing their coupling relationships

while task-specific branches were used to predict each load type separately. Experimental results based on public datasets demonstrate that the joint forecasting of cooling

heating

and electricity loads achieved reductions in mean absolute percentage error （MAPE） of 36.30%

23.93%

and 22.63%

respectively

compared to single-task prediction models. This method not only enhances the accuracy and stability of load forecasting but also better reflects the multi-energy coupling characteristics within integrated energy systems.

关键词

Keywords

references

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