LightGBM-based line loss prediction model for distribution networks

Xiaogang Wu; Zuxin Li; Xiaoqing Zhou; Qingfeng Ji; Shuangshuang Mao; Xiaoming Ju

doi:10.1117/12.3004619

16 August 2023 LightGBM-based line loss prediction model for distribution networks

Xiaogang Wu, Zuxin Li, Xiaoqing Zhou, Qingfeng Ji, Shuangshuang Mao, Xiaoming Ju

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870F (2023) https://doi.org/10.1117/12.3004619
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Accurate calculation of line loss in distribution networks can better guide the power system on how to optimize the power grid and how to carry out technical loss reduction work. In response to the problems of many electrical parameters, complicated steps and low accuracy of results required for the calculation of theoretical line loss values in traditional distribution networks, this paper proposes a distribution network line loss prediction method based on the light gradient boosting machine (LightGBM) model algorithm. The method uses machine learning models to model key electrical parameters and line loss values to automatically calculate the grid line loss. To address the difficulty of tuning the LightGBM model, we use a Bayesian optimization algorithm to adjust the model parameters. In this paper, grid data from the Kaggle data platform is used for analysis and validation, and the experimental results show that the model proposed in this paper has higher prediction accuracy than the traditional BP model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaogang Wu, Zuxin Li, Xiaoqing Zhou, Qingfeng Ji, Shuangshuang Mao, and Xiaoming Ju "LightGBM-based line loss prediction model for distribution networks", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870F (16 August 2023); https://doi.org/10.1117/12.3004619

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