A Forecasting Method for Rainfall Distribution at Four Rainfall Stations in Xining Area Based on BP Neural Network

2021 ◽  
pp. 275-292
Author(s):  
Zhuang Xiong ◽  
Jun Ma ◽  
Bingrong Zhou ◽  
Lingfei Zhang ◽  
Bohang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rainfall Distribution ◽  
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 case study on a hybrid wind speed forecasting method using BP neural network

2011 ◽  
Vol 24 (7) ◽  
pp. 1048-1056 ◽  
Author(s):  
Zhen-hai Guo ◽  
Jie Wu ◽  
Hai-yan Lu ◽  
Jian-zhou Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Wind Speed Forecasting ◽  
Forecasting Method

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.

2124 Aluminum Alloy Aging Forming Springback Study Based on a Combination Forecasting Method

2014 ◽  
Vol 541-542 ◽  
pp. 277-282
Author(s):  
Zhong Gan ◽  
Zhi Wei Qian ◽  
Yu Shan Xia
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Uniform Design ◽  
Prediction Method ◽  
Combination Method ◽  
Forecasting Method ◽  
Bending Radius ◽  
Springback Prediction

This paper proposes a more accurate springback prediction method of ageing forming for 2124 aluminum alloy. In age forming of panels, pre-bending radius, aging time and wall thickness of panels are selected as three parameters, make use of uniform design to arrange experiment and obtain springback radius using ABAQUS simulation. By means of regression analysis, the data is processed to get the influence caused by parameters on springback radius. Regression and BP neural network forecasting method are used respectively to predict springback radius and maximum prediction error is less than 31%. Combination method based on BP neural network is adopted and this method gets the satisfying prediction results that prediction error is within 5%. So conclusion can be drawn that prediction accuracy of combination method is much better than that of regression and BP neural network forecasting.

scholarly journals An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Energies ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1542 ◽  
Author(s):  
Honglu Zhu ◽  
Weiwei Lian ◽  
Lingxing Lu ◽  
Songyuan Dai ◽  
Yang Hu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Time Window ◽  
Photovoltaic Power ◽  
Forecasting Method
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