电力建设2026，Vol.47Issue(8)：52-65,14.DOI:10.12204/j.issn.1000-7229.2026.08.005
基于贝叶斯优化异构堆叠集成学习的多场景配电网规划评价
Evaluation of Multi-Scenario Distribution Network Planning Based on Bayesian Optimization of Heterogeneous Stacked Ensemble Learning
摘要
Abstract
[Objective]With the large-scale integration of renewable energy sources,diverse flexible loads,and digital-intelligent equipment,traditional distribution network planning evaluation methods struggle to flexibly adapt to the multifaceted and differentiated characteristics of modern distribution networks.To achieve data-driven,scenario-based,and differentiated planning evaluations,this study constructs an indicator system tailored to new-type distribution networks and proposes a multi-scenario planning evaluation method based on Bayesian optimization and heterogeneous stacked ensemble learning.[Methods]First,a scenario-aware feature space is constructed via one-hot encoding to enable unified ensemble learning across multiple scenarios.Second,a two-layer stacked ensemble strategy is proposed:the first layer establishes a diverse base model pool comprising Random Forest,XGBoost,LightGBM,and CatBoost,balancing the variance reduction of Bagging with the bias reduction of Boosting;the second layer employs an XGBoost meta-learner for decision fusion to integrate complementary algorithmic strengths.Furthermore,Bayesian optimization based on Gaussian Process Regression is utilized to iteratively tune hyperparameters across all layers,enhancing overall model performance and generalization capability.[Results]Case study results demonstrate that the proposed integrated model achieves an accuracy of 91.33%,representing an average improvement of 12.35%over traditional single models.Across various scenarios,accuracy remains stable within a narrow range of 90.61%-91.99%,indicating no overfitting or maladaptation in specific scenarios.[Conclusions]The proposed method effectively integrates distribution network planning scenario characteristics and leverages the complementary advantages of multiple algorithms.It successfully addresses multi-scenario,high-dimensional,and nonlinear decision-making problems in distribution network planning,demonstrating robust overall performance and superior generalization capabilities.关键词
配电网规划评价/堆叠集成学习/场景感知/XGBoost/异构融合/贝叶斯优化Key words
distribution network planning evaluation/stacked ensemble learning/scene awareness/XGBoost/heterogeneous fusion/Bayesian optimization分类
信息技术与安全科学引用本文复制引用
陈果,熊炜,张超,袁旭峰,陆之洋,罗宁..基于贝叶斯优化异构堆叠集成学习的多场景配电网规划评价[J].电力建设,2026,47(8):52-65,14.基金项目
国家自然科学基金项目(52367005) （52367005）
贵州省科技支撑项目(黔科合支撑[2024]一般049) （黔科合支撑[2024]一般049）
国家自然科学基金项目(62461008) （62461008）
南网创新项目(GZKJXM20222468) This work is supported by National Natural Science Foundation of China(No.52367005),Science and Technology Support Program of Guizhou Province(No.[2024]General 049),National Natural Science Foundation of China(No.62461008)and Innovation Program of China Southern Power Grid Co.,Ltd.(No.GZKJXM20222468). （GZKJXM20222468）
