基于时序重构与TGA-AdaBoost的综合能源系统多元负荷预测方法

韩璐; 罗晶; 杨轶航; 王堃擘; 马艺丹

doi:10.19912/j.0254-0096.tynxb.2025-0813

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基于时序重构与TGA-AdaBoost的综合能源系统多元负荷预测方法

更新时间：2026-10-15

- 基于时序重构与TGA-AdaBoost的综合能源系统多元负荷预测方法
- Vol. 47, Issue 9, Pages: 640-651(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0813
  CLC： TK01
- Online First：10 October 2026，
  
  Published：2026
- 稿件说明：
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韩璐, 罗晶, 杨轶航, et al. 基于时序重构与TGA-AdaBoost的综合能源系统多元负荷预测方法[J]. 2026, 47(9): 640-651.
DOI：

韩璐, 罗晶, 杨轶航, et al. 基于时序重构与TGA-AdaBoost的综合能源系统多元负荷预测方法[J]. 2026, 47(9): 640-651. DOI： 10.19912/j.0254-0096.tynxb.2025-0813.

摘要

准确预测综合能源系统中的各种负荷需求对其经济稳定运行至关重要。然而

用能形式的多样化发展使负荷演化趋势日益复杂

为准确预测带来严峻挑战。为此

提出一种结合时序重构技术、组合神经网络与集成学习的预测架构。时序重构技术由改进自适应噪声完备集合经验模态分解（ICEEMDAN）和样本熵算法（SE）组成

可将复杂的原始序列分解并重构为更平滑的子序列

以降低预测难度。组合神经网络结合了时间卷积网络（TCN）、门控循环单元（GRU）和注意力机制（AM）的优势

用于捕捉子序列的长期依赖关系。同时

通过AdaBoost算法增强模型的鲁棒性和泛化能力。最后

将所有子序列的预测结果累加得出最终结果。对比实验结果表明

所提模型的电、气、热和冷负荷预测效果均优于其他对比模型。此外

相较于GRU基准模型

所提模型的平均绝对百分比误差与均方根误差分别降低40.59%与40.03%。

Abstract

Accurate prediction of various load demands in an integrated energy system is crucial to its stable economic operation. However

the diversified development of energy use forms makes the load evolution trend increasingly complex

which brings severe challenges to accurate prediction. To this end

a prediction architecture combining time series reconstruction technology

a combined neural network and ensemble learning is proposed. The time series reconstruction technology is composed of improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN） and sample entropy algorithm （SE）

which decomposes and reconstructs the complex original sequence into smoother subsequences to reduce the difficulty of prediction. The combined neural network combines the advantages of temporal convolutional network （TCN）

gated recurrent unit （GRU） and attention mechanism（AM） to capture the long-term dependencies of subsequences. At the same time

the robustness and generalization ability of the model are enhanced by the adaptive Boosting algorithm. Finally

the prediction results of all subsequences are accumulated to obtain the final result. The results of comparative experiments show that the proposed model is superior to other comparative models in the prediction of electrical

gas

heat and cooling loads. In addition

compared with the GRU benchmark model

the mean absolute percentage error and root mean square error of the proposed model are reduced by 40.59% and 40.03%

respectively.

关键词

Keywords

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