Ice Coating Prediction Based on Two-Stage Adaptive Weighted Ensemble Learning

被引:1
|
作者
Guo, Heng [1 ]
Cui, Qiushi [1 ]
Shi, Lixian [1 ]
Parol, Jafarali [2 ]
Alsanad, Shaikha [2 ]
Wu, Haitao [3 ]
机构
[1] Chongqing Univ, Sch Elect Engn, Chongqing 400044, Peoples R China
[2] Kuwait Inst Sci Res, Kuwait 999044, Kuwait
[3] State Grid Chongqing Elect Power Co, Elect Power Sci Res Inst, Chongqing 400020, Peoples R China
关键词
transmission line; ice coating prediction; data driven; multi-meteorological scenario; ensemble learning; MACHINE;
D O I
10.3390/pr12091854
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Severe ice accretion on transmission lines can disrupt electrical grids and compromise the stability of power systems. Consequently, precise prediction of ice coating on transmission lines is vital for guiding their operation and maintenance. Traditional single-model icing prediction methods often exhibit limited accuracy under varying environmental conditions and fail to yield highly accurate predictions. We propose a multi-scenario, two-stage adaptive ensemble strategy (MTAES) for ice coating prediction to address this issue. A combined clustering approach is employed to refine the division of ice weather scenarios, segmenting historical samples into multiple scenarios. Within each scenario, the bagging approach generates multiple training subsets, with the extreme learning machine (ELM) used to build diverse models. Subsequently, a two-stage adaptive weight allocation mechanism is introduced. This mechanism calculates the distance from the scenario cluster centers and the prediction error of similar samples in the validation set for each test sample. Weights are dynamically allocated based on these data, leading to the final output results through an adaptive ensemble from the base model repository. The experimental results show that the model is significantly better than traditional models in predicting ice thickness. Key indicators of RMSE, MAE, and R2 reach 0.675, 0.522, and 83.2%, respectively, verifying the effectiveness of multi-scene partitioning and adaptive weighting methods in improving the accuracy of ice cover prediction.
引用
收藏
页数:20
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