scholarly journals Short-term power load forecasting based on IVL-BP neural network technology

2012 ◽  
Vol 4 ◽  
pp. 168-174 ◽  
Author(s):  
Yongli Wang ◽  
Dongxiao Niu ◽  
Li Ji
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Network Technology ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Neural Network Technology

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.

Applied Research Of BP Neural Network In Earthquake Prediction

2012 ◽  
Vol 204-208 ◽  
pp. 2449-2454 ◽  
Author(s):  
Wu Sheng Hu ◽  
Hong Lin Nie ◽  
Hao Wang
Keyword(s):  
Neural Network ◽  
Earthquake Prediction ◽  
Bp Neural Network ◽  
Integrated Model ◽  
Earthquake Magnitude ◽  
Linear Regression Method ◽  
Network Technology ◽  
Short Term ◽  
Non Linear ◽  
Neural Network Technology

Nowadays, earthquake prediction is still a worldwide scientific problem, especially the prediction for short-term and imminent earthquake has no substantial breakthroughs. BP neural network technology has a strong non-linear mapping function which could better reflect the strong non-linear relationship between earthquake precursors and the time and the magnitude of a potential earthquake. In this paper, we selected the region of Beijing as the research area and 3 months as the prediction period. Based on BP neural network and integrated with the conventional linear regression method, a regional short-term integrated model was established, which gives the quantitative prediction for the earthquake magnitude. The results show that the earthquake magnitude prediction RMSE (root mean square error) of the integrated model reaches ± 0.28 Ms. Compared with conventional methods, the integrated model improves significantly. The new model has a good prospect to use BP neural network technology for earthquake prediction.

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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