An Efficient Algorithm for Power Prediction in PV Generation System

2020 ◽  
Vol 9 (2) ◽  
pp. 207-216
Author(s):  
Qais Alsafasfeh
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Weather Conditions ◽  
Cuckoo Search ◽  
Wavelet Neural Network ◽  
Mean Values ◽  
Neural Network Algorithm ◽  
The Mean ◽  
Pv Generation ◽  
Error Indicators

Aiming at the existing photovoltaic power generation prediction methods, the modeling is complicated, the prediction accuracy is low, and it is difficult to meet the actual needs. Based on the improvement of the traditional wavelet neural network, a dual-mode cuckoo search wavelet neural network algorithm combined prediction method is proposed, which takes into account the extraction of chaotic features of surface solar radiation and photovoltaic output power. The proposed algorithm first reconstructs the chaotic phase space of the hidden information of each influencing factor in the data history of PV generation and according to the correlation analysis, the solar radiation is utilized as additional input. Next, the proposed algorithm overcomes the limitations of the cuckoo search algorithm such as the sensitivity to the initial value and searchability and convergence speed by dual-mode cuckoo search wavelet neural network algorithm. Lastly, a prediction model of the proposed algorithm is proposed and the prediction analysis is performed under different weather conditions. Simulation results show that the proposed algorithm shows better performance than the existing algorithms under different weather conditions. Under various weather conditions, the mean values of TIC, EMAE and ENRMSE error indicators of the proposed forecasting algorithm were reduced by 43.70%, 45.75%, and 45.41%, respectively. Compared with the Chaos-WNN prediction method, the prediction performance has been further improved under various weather conditions and the mean values of TIC, EMAE and ENRMSE error indicators have been reduced by 25.55%, 27.26%, and 36.83%, respectively. ©2020. CBIORE-IJRED. All rights reserved

scholarly journals Research on bus elastic departure interval based on Wavelet Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03052
Author(s):  
Zhe Lv ◽  
Jiayu Zou ◽  
Zhongyu Zhao
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Economic Costs ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Intelligent Optimization Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Important Basis

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained.

scholarly journals EQUIPMENT PRICE FORECAST BASED ON T-S FUZZY NEURAL NETWORK

2018 ◽  
Vol 48 (4) ◽  
pp. 305-309
Author(s):  
G. P. JIANG ◽  
L. XIE ◽  
S. X. SUN
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Test Results ◽  
Factors Affecting ◽  
Price Forecast ◽  
Network Algorithm ◽  
Auction Price ◽  
Variable Weight ◽  
Fuzzy Neural ◽  
Neural Network Algorithm

As we all know, the factors affecting the price of equipment are more complicated, but these factors still have a great correlation. How can we accurately predict the price of equipment? Based on the study of the tight support and smoothness of wavelet function, this paper proposes a correlation variable weight wavelet neural network algorithm to predict the price of 162 devices. The test results show that if the weight is not reduced, the predicted price is 0, and the error is still large. However, by arranging the data from small to large, the variable weighted wavelet neural network algorithm is used to predict the result closer to the auction price, which overcomes the incompatibility of the algorithm iteration and provides a reference for accurately predicting the price of the device.

scholarly journals Research on improved convolutional wavelet neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingwei Liu ◽  
Peixuan Li ◽  
Xuehan Tang ◽  
Jiaxin Li ◽  
Jiaming Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Radial Basis Function ◽  
Error Rate ◽  
Basis Function ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Mean Square ◽  
The Mean

AbstractArtificial neural networks (ANN) which include deep learning neural networks (DNN) have problems such as the local minimal problem of Back propagation neural network (BPNN), the unstable problem of Radial basis function neural network (RBFNN) and the limited maximum precision problem of Convolutional neural network (CNN). Performance (training speed, precision, etc.) of BPNN, RBFNN and CNN are expected to be improved. Main works are as follows: Firstly, based on existing BPNN and RBFNN, Wavelet neural network (WNN) is implemented in order to get better performance for further improving CNN. WNN adopts the network structure of BPNN in order to get faster training speed. WNN adopts the wavelet function as an activation function, whose form is similar to the radial basis function of RBFNN, in order to solve the local minimum problem. Secondly, WNN-based Convolutional wavelet neural network (CWNN) method is proposed, in which the fully connected layers (FCL) of CNN is replaced by WNN. Thirdly, comparative simulations based on MNIST and CIFAR-10 datasets among the discussed methods of BPNN, RBFNN, CNN and CWNN are implemented and analyzed. Fourthly, the wavelet-based Convolutional Neural Network (WCNN) is proposed, where the wavelet transformation is adopted as the activation function in Convolutional Pool Neural Network (CPNN) of CNN. Fifthly, simulations based on CWNN are implemented and analyzed on the MNIST dataset. Effects are as follows: Firstly, WNN can solve the problems of BPNN and RBFNN and have better performance. Secondly, the proposed CWNN can reduce the mean square error and the error rate of CNN, which means CWNN has better maximum precision than CNN. Thirdly, the proposed WCNN can reduce the mean square error and the error rate of CWNN, which means WCNN has better maximum precision than CWNN.

BP Neural Network Based Animation Production Prediction

2014 ◽  
Vol 539 ◽  
pp. 475-478
Author(s):  
Ran Tao ◽  
Da Chao Yuan ◽  
Gang Yi Hu
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Coefficient Of Determination ◽  
Mean Relative Error ◽  
Training Samples ◽  
The Mean ◽  
Production Prediction

In order to research the basic condition of animation production, this article chooses BP Neural Network to predict the animation production. We select 13 test samples, selected nine of them randomly as training samples, and the remaining four as the test samples. The coefficient of determination is 0.99839 and the mean relative error is 0.186125. The result shows that BP Neural Network is an effective prediction method.

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