Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

2018 ◽  
Author(s):  
Chen CunLi ◽  
Luo Xiong ◽  
Zhang Yan ◽  
Zhou Jie ◽  
Chen Yong ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Wind Field ◽  
Grey Prediction ◽  
Wind Lidar

scholarly journals Application of Grey Prediction and BP Neural Network in Hydrologic Prediction

2015 ◽  
Vol 25 ◽  
pp. 01002
Author(s):  
Jun Huang ◽  
Pingwei Jin ◽  
Jiaping Xiang ◽  
Lanbin Li ◽  
Xuebing Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Grey Prediction ◽  
Hydrologic Prediction

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.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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