现代电力2026，Vol.43Issue(3)：464-474,11.DOI:10.19725/j.cnki.1007-2322.2024.0038
基于XGBoost与多源域迁移学习的贫资料地区负荷预测方法
Load Forecasting in Data-scarce Areas Based on Multi-source Transfer Learning and XGBoost
摘要
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
负荷预测/迁移学习/极限梯度提升树/可迁移性度量/夏普利可加性解释Key words
load forecasting/transfer learning/extreme gradient boosting(XGBoost)/transferability measure/Shapley additive explanations(SHAP)分类
信息技术与安全科学引用本文复制引用
唐義坤,唐志远,罗同桐,谯傲,刘俊勇..基于XGBoost与多源域迁移学习的贫资料地区负荷预测方法[J].现代电力,2026,43(3):464-474,11.基金项目
四川省科技计划项目(2023YFSY0033).Project Supported by Sichuan Science and Technology Program of Sichuan Province(2023YFSY0033). （2023YFSY0033）
