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

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.

Dynamic Monitoring of Low Voltage Distribution Network Leakage Detection Device Based on Internet of Things Technology

“…Among them, RFID, EPC, wireless sensor network, ZigBee, Mobile Internet, M2M, Ethernet, serial communication technology are widely used. According to this research demand, the sensor sensing technology in Internet of Things technology is used to obtain the data of low-voltage distribution network leakage detection device [5] .…”

An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

“…In this paper, the DBN-DNN model source training data adopts the data of August 2019 (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), and the data of the task to be calculated adopts the data of August 21, 2020. Due to the randomness of the output of wind power and photovoltaic power, the model training and calculation are difficult.…”

“…The authors in [16] proposes a joint network loss rate calculation method for vertical-lateral error matching, which improves the calculation accuracy, but there are insufficient calculation speeds when processing highdimensional data. The authors in [17][18] proposes a calculation method based on neural network, which solves the problem of calculation speed in high-dimensional data, but it has a strong dependence on initial parameters, and improper selection of initial data will produce larger errors. Therefore, genetic algorithms are often used.…”

Energy Loss Calculation of Low Voltage Distribution Area Based on Variational Mode Decomposition and Least Squares Support Vector Machine

“…Data-driven feature selection methods such as the grey correlation method provide a solution to this problem. Yao and Zhu [11] applied gradient boosting decision tree model in line loss predicting in LV-area. And the author demonstrated that the method proposed performs better than others' traditional subjective feature selection methods.…”