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Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree

Authors :
Mengting Yao
Yun Zhu
Junjie Li
Hua Wei
Penghui He
Source :
Energies, Vol 12, Iss 13, p 2522 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

Line loss rate plays an essential role in evaluating the economic operation of power systems. However, in a low voltage (LV) distribution network, calculating line loss rate has become more cumbersome due to poor configuration of the measuring and detecting device, the difficulty in collecting operational data, and the excessive number of components and nodes. Most previous studies mainly focused on the approaches to calculate or predict line loss rate, but rarely involve the evaluation of the prediction results. In this paper, we propose an approach based on a gradient boosting decision tree (GBDT), to predict line loss rate. GBDT inherits the advantages of both statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. An empirical study on a data set in a city demonstrates that our proposed approach performs well in predicting line loss rate, given a large number of unlabeled examples. Experiments and analysis also confirmed the effectiveness of our proposed approach in anomaly detection and practical project management.

Details

Language :
English
ISSN :
19961073
Volume :
12
Issue :
13
Database :
Directory of Open Access Journals
Journal :
Energies
Publication Type :
Academic Journal
Accession number :
edsdoj.31b4bc02be6d40119b15b6dc66c12fec
Document Type :
article
Full Text :
https://doi.org/10.3390/en12132522