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
相关论文
共 50 条
  • [1] Heterogeneous defect prediction with two-stage ensemble learning
    Zhiqiang Li
    Xiao-Yuan Jing
    Xiaoke Zhu
    Hongyu Zhang
    Baowen Xu
    Shi Ying
    Automated Software Engineering, 2019, 26 : 599 - 651
  • [2] Heterogeneous defect prediction with two-stage ensemble learning
    Li, Zhiqiang
    Jing, Xiao-Yuan
    Zhu, Xiaoke
    Zhang, Hongyu
    Xu, Baowen
    Ying, Shi
    AUTOMATED SOFTWARE ENGINEERING, 2019, 26 (03) : 599 - 651
  • [3] Data-driven decision model based on local two-stage weighted ensemble learning
    Xu, Che
    Chang, Wenjun
    Liu, Weiyong
    ANNALS OF OPERATIONS RESEARCH, 2023, 325 (02) : 995 - 1028
  • [4] Data-driven decision model based on local two-stage weighted ensemble learning
    Che Xu
    Wenjun Chang
    Weiyong Liu
    Annals of Operations Research, 2023, 325 : 995 - 1028
  • [5] A two-stage stacked-based heterogeneous ensemble learning for cancer survival prediction
    Yan, Fangzhou
    Feng, Yi
    COMPLEX & INTELLIGENT SYSTEMS, 2022, 8 (06) : 4619 - 4639
  • [6] A two-stage stacked-based heterogeneous ensemble learning for cancer survival prediction
    Fangzhou Yan
    Yi Feng
    Complex & Intelligent Systems, 2022, 8 : 4619 - 4639
  • [7] Two-Stage Adaptive Ensemble Learning Method for Different Types of Concept Drift
    Guo, Husheng
    Zhang, Yang
    Wang, Wenjian
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2024, 61 (07): : 1799 - 1811
  • [8] A two-stage multiobjective evolutionary ensemble learning for silicon prediction in blast furnace
    Qiang Li
    Jingchuan Zhang
    Wenhao Wang
    Xianpeng Wang
    Complex & Intelligent Systems, 2024, 10 : 1639 - 1660
  • [9] A two-stage multiobjective evolutionary ensemble learning for silicon prediction in blast furnace
    Li, Qiang
    Zhang, Jingchuan
    Wang, Wenhao
    Wang, Xianpeng
    COMPLEX & INTELLIGENT SYSTEMS, 2024, 10 (01) : 1639 - 1660
  • [10] Improving imbalance classification via ensemble learning based on two-stage learning
    Liu, Na
    Wang, Jiaqi
    Zhu, Yongtong
    Wan, Lihong
    Li, Qingdu
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2024, 17