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OA

[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.