Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning

• Published in: International journal of engineering and technology innovation Vol. 14; no. 1; pp. 103 - 114
• Main Authors: Jian-Yu Ren, Jian-Wei Zhao, Nan Pan, Nuo-Bin Zhang, Jun-Wei Yang
• Format: Journal Article
• Language: English
• Published: Taiwan Association of Engineering and Technology Innovation 01.01.2024
• Subjects: integrated learning; K-means clustering; line loss rate prediction; LSTM; XGBoost
• ISSN: 2223-5329; 2226-809X
• DOI: 10.46604/ijeti.2023.12869

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.