An ensemble-based outlier detection method for clustered and local outliers with differential potential spread loss

Gao, Xin; Yu, Jiahao; Zha, Sen; Fu, Shiyuan; Xue, Bing; Huang, Zijian; Zhang, Guangyao

doi:10.1016/j.knosys.2022.110003

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…The study first identifies and cleanses the original "dirty data," and the common data cleaning methods are statistical 3σ criterion, box plots, and clustering methods based on machine learning, local anomaly factors, isolated forests, and deep learning methods [5][6][7][8]. Due to the diversity of line loss problems, false alarms, omissions, and other problems in the detection process, the above methods present a significant human impact on anomaly data detection.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning

Jian-Yu Ren,

Jian-Wei Zhao,

Nan Pan

et al. 2023

Int. j. eng. technol. innov.

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The distribution network line loss rate is a crucial factor in improving the economic efficiency of power grids. However, the traditional prediction model has low accuracy. This study proposes a predictive method based on data preprocessing and model integration to improve accuracy. Data preprocessing employs dynamic cleaning technology with machine learning to enhance data quality. Model integration combines long short-term memory (LSTM), linear regression, and extreme gradient boosting (XGBoost) models to achieve multi-angle modeling. This study employs regression evaluation metrics to assess the difference between predicted and actual results for model evaluation. Experimental results show that this method leads to improvements over other models. For example, compared to LSTM, root mean square error (RMSE) was reduced by 44.0% and mean absolute error (MAE) by 23.8%. The method provides technical solutions for building accurate line loss monitoring systems and enhances power grid operations.

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Section: Introductionmentioning

confidence: 99%