Short-Term Bus Load Forecasting Method Based on CNN-GRU Neural Network

2019 ◽  
pp. 711-722 ◽  
Author(s):  
Maoya Shen ◽  
Qifeng Xu ◽  
Kaijie Wang ◽  
Mengfu Tu ◽  
Bingxiang Wu
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Method

A Short-Term Power Load Forecasting Method Based on BP Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 1647-1650 ◽  
Author(s):  
Ling Juan Li ◽  
Wen Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Load Forecasting ◽  
High Accuracy ◽  
Back Propagation Algorithm ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

A Forecasting Method of Short-Term Electric Power Load Based on BP Neural Network

2014 ◽  
Vol 538 ◽  
pp. 247-250 ◽  
Author(s):  
Hou Bin ◽  
Yun Xiao Zu ◽  
Chao Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Weather Factors ◽  
Full Account ◽  
Power Load ◽  
Forecasting Method ◽  
Neural Network Theory ◽  
Electric Power Load

Described the meaning of the Short-Term Power forecasting firstly, then gives summary of the basic principles and steps of the power load forecasting, analyses the disadvantages of traditional forecasting methods, and proposing the load analysis plan base on BP neural network theory. Taking full account of the relationship between the daily load and weather factors, establishes a short-term load forecasting model. Results of the prediction are verified highly precise and stable, which makes it suitable for different forecasting conditions.

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