Load Forecasting of Integrated Energy System Based on Combination of Decomposition Algorithms and Meta-learning

HUANG Huang; ZHANG An’an

doi:10.7500/AEPS20231116003

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Load Forecasting of Integrated Energy System Based on Combination of Decomposition Algorithms and Meta-learning

Basic Research | 更新时间：2025-06-16

- Load Forecasting of Integrated Energy System Based on Combination of Decomposition Algorithms and Meta-learning
- Automation of Electric Power Systems Vol. 48, Issue 10, Pages: 151-160(2024)
- 作者机构：
  
  西南石油大学电气信息学院，四川省成都市 610500
- 作者简介：
- 基金信息：
  
  Sichuan Provincial Science and Technology Plan(24NSFSC4924)
- DOI：10.7500/AEPS20231116003
  CLC：
- Received：16 November 2023，
  
  Revised：2024-01-15，
  
  Published Online：10 April 2024，
  
  Published：25 May 2024
- 稿件说明：
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黄璜,张安安.基于分解算法与元学习结合的综合能源系统负荷预测[J].电力系统自动化,2024,48(10):151-160.

HUANG Huang,ZHANG An’an.Load Forecasting of Integrated Energy System Based on Combination of Decomposition Algorithms and Meta-learning[J].Automation of Electric Power Systems,2024,48(10):151-160.
黄璜,张安安.基于分解算法与元学习结合的综合能源系统负荷预测[J].电力系统自动化,2024,48(10):151-160. DOI： 10.7500/AEPS20231116003.

HUANG Huang,ZHANG An’an.Load Forecasting of Integrated Energy System Based on Combination of Decomposition Algorithms and Meta-learning[J].Automation of Electric Power Systems,2024,48(10):151-160. DOI： 10.7500/AEPS20231116003.

摘要

针对区域综合能源系统(IES)负荷间关联敏感性高、季节泛化性差导致的负荷预测精度受限问题，提出一种基于分解算法与元学习结合的多元负荷组合预测方法。首先，基于动态最大信息系数量化不同时段多元负荷间相关性，根据动态相关性结果构造特征输入变量。然后，通过窗口滑动将负荷序列分成多个子序列单元，并使用变分模态分解将其划分为多个任务，避免整体分解带来的前瞻性偏差问题。最后，采用双向长短期记忆模型预测子序列，并通过模型无关的元学习算法减少梯度迭代，重构子序列后融合全连通层输出预测结果。基于美国亚利桑那州立大学坦佩校区IES数据集

验证了所提混合模型具有更高的IES多元负荷预测精度。

Abstract

Aiming at the problem of limited load forecasting accuracy due to high sensitivity of inter-load correlation and poor seasonal generalization in the regional integrated energy system (IES)

a multivariate load combination forecasting method based on the combination of decomposition algorithms and meta-learning is proposed. First

the correlation between multivariate loads in different time periods is quantified based on the dynamic maximal information coefficient

and the characteristic input variables are constructed according to the dynamic correlation results. Then

the load sequence is divided into multiple sub-sequence units by window sliding and divided into multiple tasks by variational modal decomposition to avoid the forward-looking bias problem caused by the overall decomposition. Finally

the subsequence is forecasted using a bi-directional long short-term memory model and the gradient iterations are reduced by a model-agnostic meta-learning algorithm

which reconstructs the subsequence and then fuses the fully connected layers to output the prediction. Based on the IES dataset of Arizona State University Tempe Campus

USA

the proposed hybrid model is verified to have higher IES multivariate load forecasting accuracy.

关键词

Keywords

references

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