基于集合学习的配电网线路损耗率预测

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
Jian-Yu Ren, Jian-Wei Zhao, Nan Pan, Nuo-Bin Zhang, Jun-Wei Yang
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引用次数: 0

摘要

配电网线损率是提高电网经济效益的关键因素。然而,传统的预测模型准确率较低。本研究提出了一种基于数据预处理和模型集成的预测方法,以提高预测精度。数据预处理采用机器学习动态清洗技术,以提高数据质量。模型集成结合了长短期记忆(LSTM)、线性回归和极梯度提升(XGBoost)模型,以实现多角度建模。本研究采用回归评估指标来评估预测结果与实际结果之间的差异,从而对模型进行评估。实验结果表明,这种方法比其他模型有所改进。例如,与 LSTM 相比,均方根误差 (RMSE) 降低了 44.0%,平均绝对误差 (MAE) 降低了 23.8%。该方法为建立精确的线损监测系统提供了技术解决方案,并提高了电网运行水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning
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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来源期刊
CiteScore
2.80
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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