Prediction of Line Loss Rate Based on Improved BP Neural Network

2022 ◽  
pp. 595-601
Author(s):  
Huoming Zhang ◽  
Ruixiang Chen ◽  
Pinglan Lu ◽  
Weibin Guan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Loss Rate ◽  
Line Loss

Study on the Prediction of Line Loss Rate Based on the Improved RBF Neural Network

2014 ◽  
Vol 915-916 ◽  
pp. 1292-1295 ◽  
Author(s):  
Ye Ren ◽  
Xiu Ge Zhang ◽  
Xun Cheng Huang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Loss Rate ◽  
Rbf Neural Network ◽  
Distribution Network ◽  
Voltage Distribution ◽  
Medium Voltage ◽  
Radial Basis ◽  
Line Loss

According to characteristics of medium voltage distribution network, use raw data that are easily collected to study an accurate fast and simple line loss calculation method of the medium voltage distribution network, that is the radial basis function neural network algorithm. In order to improve the power system line loss rate accuracy, the paper puts forward using alternating gradient algorithm to improve the radial basis function (RBF) neural network. The simulation results show that the algorithm is feasible.

Line Loss Rate Forecasting Based on Grey Model and Combination of Neural Network

2012 ◽  
Vol 621 ◽  
pp. 340-343 ◽  
Author(s):  
Fei Xie ◽  
Bu Xiang Zhou ◽  
Qin Zhang ◽  
Long Jiang
Keyword(s):  
Neural Network ◽  
Loss Rate ◽  
Grey Model ◽  
Combined Model ◽  
Related Factors ◽  
The Neural Network ◽  
Line Loss ◽  
Input Variables ◽  
Relationship Of ◽  
Bp Model

This thesis is mainly focusing on the research of the method for line loss rate forecast by adopting grey model combined with neural network. Firstly, GM(1,1) model can be used to analyze and calculate line loss rate change trend. The input variables of the neural network could be determined by grey relationship of related factors. Three-Layer BP model for line loss rate forecast is constructed, and then the eventual result can be obtained by using the combined model of GM(1,1) and neural network method. An example is taken to prove the precision improved for line loss rate forecast by the proposed method studied in the thesis.

scholarly journals Application of Grey Prediction in a GA-BP Power Theft Algorithm

2020 ◽  
Author(s):  
Xiaofeng Chen ◽  
Zhongping Xu ◽  
Lipeng Zhang ◽  
Feng Zhu ◽  
Xiaoming Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Power Grid ◽  
Absolute Error ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Line Loss ◽  
Predicted Values

Statistics show that power theft is one of the main reasons for the dramatic increase in power grid line loss. In this paper, a genetic algorithm is used to optimize a neural network and establish a power theft prediction model. With the grey prediction model, the predicted values of variables are obtained and then applied to the prediction model of a GA-BP neural network to obtain relatively accurate predictions from limited samples, reducing the absolute error. Through the two levels of prediction and analysis, the model is demonstrated to have good universality in predicting power theft behavior, and is a practical and effective method for power companies to carry out power theft analysis.

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