Load Forecasting in Data-scarce Areas Based on Multi-source Transfer Learning and XGBoost

TANG Yikun; TANG Zhiyuan; LUO Tongtong; QIAO Ao; LIU Junyong

doi:10.19725/j.cnki.1007-2322.2024.0038

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Load Forecasting in Data-scarce Areas Based on Multi-source Transfer Learning and XGBoost

更新时间：2026-06-02

- Load Forecasting in Data-scarce Areas Based on Multi-source Transfer Learning and XGBoost
- Vol. 43, Issue 3, Pages: 464-474(2026)
- 作者机构：
  
  1. 四川大学电气工程学院,四川省,成都市,610065
  2. 成都信息工程大学物流学院,四川省,成都市,610065
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0038
  CLC： 3
- Published：2026
- 稿件说明：
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TANG Yikun, TANG Zhiyuan, LUO Tongtong, et al. Load Forecasting in Data-scarce Areas Based on Multi-source Transfer Learning and XGBoost[J]. 2026, 43(3): 464-474.
DOI：

TANG Yikun, TANG Zhiyuan, LUO Tongtong, et al. Load Forecasting in Data-scarce Areas Based on Multi-source Transfer Learning and XGBoost[J]. 2026, 43(3): 464-474. DOI： 10.19725/j.cnki.1007-2322.2024.0038.

摘要

准确的负荷预测对保障电力系统安全稳定运行非常关键，贫资料地区由于缺乏相关的有效数据集，难以使用传统的机器学习预测方法。针对小样本条件下的负荷预测问题，提出一种基于极限梯度提升树（extreme gradient boosting，XGBoost）与多源域迁移学习框架。首先，为度量源域与目标域之间的可迁移性，利用XGBoost构建域数据的特征–输出映射关系，并引入一种基于夏普利可加性解释（Shapley additive explanations，SHAP）值分布的相似性度量方法，计算域之间特征–输出映射关系的相似性。然后，选择与目标域高度相似的多个源域模型作为迁移模型，并通过最优组合系数将各个源域模型的输出结果集成，以提高整体的迁移效果。最后，使用开源数据对所提出的框架进行性能评估和有效性验证，相比于多层感知机（multi-layer perception，MLP）、支持向量回归（support vector regression，SVR）、长短期记忆网络（long short-term memory network，LSTM）和XGBoost方法的直接预测，所提方法能提高至少10%的预测精度；相较于基于形态相似性的迁移学习方法，所提方法能提升选择有效源域的准确性。

Abstract

Accurate electric load forecasting

especially with small sample sets

is a critical topic in the field of power system. In this paper

we propose an attentive multi-source transfer learning framework based on extreme gradient boosting (XGBoost) for load forecasting in data-scarce scenarios. In the proposed framework

a new quantifiable framework is developed by exploiting the feature-output mapping relationship constructed by XGBoost. To effectively assess the transferability across domains

a similarity metric is introduced grounded in SHAP value distribution. Models of multiple sources that exhibit high similarity to the target domain are selected as the transfer models

and attentively integrated using the optimal combining coefficients

aiming to enhance overall transfer performance. Experiments conducted on real-world data sets illustrate that

compared with another popular transfer learning method

the proposed framework not only effectively identifies the potential source tasks that can produce positive transfer

but also achieves improved forecasting accuracy for residential electric load with limited data.

关键词

Keywords

references

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