Research on the Prediction Model of Urban Power Grid Short-Term Load Based on BP Neural Network Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 954-957
Author(s):  
Pei Long Xu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Power Grid ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Short Term ◽  
Network Algorithm ◽  
Neural Network Algorithm

Objective: The paper aims to establish the prediction model of urban power grid short-term load based on BP neural network algorithm. Method: Five factors influencing the urban power grid short-term load are used to establish the neural network model: date type, weather, daily maximum temperature, daily minimum temperature and daily average temperature. Matlab toolbox is used to develop the testing platform through VC++ programming. Result: The variable learning rates are 0.35 and 0.64. With 23410 iterations, the model is converged, and the global error is 0.00032. Conclusion: Through the data comparison and analysis, the relative error is within 5%, thus indicating the model is accurate and effective, and it can be used to predict the change of urban power grid short-term load.

scholarly journals Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jinjuan Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Training Model ◽  
Sports Performance ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

There are many factors that affect athletes’ sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes’ problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.

scholarly journals Research on Temperature Field Prediction Model of Electric Spindle Based on Improved BP Neural Network

2021 ◽  
Author(s):  
Enwen Zhou ◽  
Yanling Zhao ◽  
Ye Dai ◽  
Jingwei Zhang ◽  
Yuan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Temperature Field ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Test Method ◽  
Motorized Spindle ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Temperature Field Prediction

Abstract The motorized spindle is the core component of CNC machine tools. In order to ensure its processing performance and processing safety, the temperature field of motorized spindle is studied. The three-dimensional model of the motorized spindle is established, and the convective heat transfer coefficient of the internal heat load and the simulation boundary condition are calculated by combining the heat transfer theory. The simulation is carried out by the finite element analysis software, and the internal temperature distribution of the motorized spindle under thermal steady state is calculated. Based on the numerical simulation analysis method and the thermal balance test method, the data basis for the prediction model of the motorized spindle temperature field is provided. The traditional BP neural network algorithm and PSO-BP neural network algorithm are used to predict the temperature of the motorized spindle measuring point under specific working conditions, and the temperature field prediction results are compared and analyzed. The results show that the PSO-BP neural network prediction model has good compatibility for variable data input, and the prediction results show little difference, which has high prediction accuracy and robustness.

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