scholarly journals Chaos-GA-BP Neural Network Power Load Forecasting Based on Rough Set Theory

2021 ◽  
Vol 2010 (1) ◽  
pp. 012132
Author(s):  
Dianwen Li ◽  
Xiu Ji ◽  
Xin Tian
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Bp Neural Network ◽  
Rough Set Theory ◽  
Load Forecasting ◽  
Power Load ◽  
Power Load Forecasting ◽  
Network Power

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 Traffic Accident Predictive Model Based on Neural Networks Algorithm and Rough Set Theory

2011 ◽  
Vol 97-98 ◽  
pp. 947-951 ◽  
Author(s):  
Qiao Ru Li ◽  
Liang Chen ◽  
Chang Guang Cheng ◽  
Yue Xiang Pan
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Bp Neural Network ◽  
Traffic Accident ◽  
Rough Set Theory ◽  
Road Traffic ◽  
Black Spot ◽  
Black Spots ◽  
Neural Network Algorithm

The most important and critical step to improve road traffic safety is prediction and identification of traffic accident black spot. A new prediction model of traffic accident black spots is proposed based on GA-BP neural network algorithm and rough set theory. First of all, the traffic accident statistics of Jinwei Road in Tianjin are analyzed. With consideration of static road conditions, the samples of road accident black spots are obtained by the GA-BP neural network algorithm. Furthermore, an effective road traffic accident black spot prediction model is established by utilizing rough set theory with consideration of the impact of real time dynamic conditions. Finally, a numerical example is illustrated. Experimental results show that the proposed model with the combination of these two theories can reduce the hybrid and burdensome amount of data, lower the false alarm rate and improve the forecasting accuracy of accident black spots.

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