A Neural Network Based Forecasting Method For the Unemployment Rate Prediction Using the Search Engine Query Data

2011 ◽  
Author(s):  
Wei Xu ◽  
Tingting Zheng ◽  
Ziang Li
Keyword(s):  
Neural Network ◽  
Search Engine ◽  
Unemployment Rate ◽  
Rate Prediction ◽  
Forecasting Method

Data mining for unemployment rate prediction using search engine query data

2012 ◽  
Vol 7 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Wei Xu ◽  
Ziang Li ◽  
Cheng Cheng ◽  
Tingting Zheng
Keyword(s):  
Data Mining ◽  
Search Engine ◽  
Unemployment Rate ◽  
Rate Prediction

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

Sign in / Sign up

Export Citation Format

Share Document