DocumentCode
3599872
Title
Study on HCPV power forecasting model based on grey neural network and Markov chain
Author
Zhengqiu Yang ; Zeping Li ; Jiapeng Xiu
Author_Institution
Beijing Univ. of Posts & Telecommun., Beijing, China
fYear
2014
Firstpage
362
Lastpage
365
Abstract
The accurate power output forecasting is advantageous to improving the reliability of power system. This paper presents a new power forecasting model based on grey neural network and Markov chain. In grey neural network, it gains the power at the corresponding time as the forecasting result. As getting the relative prediction residual errors of the forecasting sample data with grey neural network, residual errors is corrected by the Markov chain method to improving the forecasting accuracy. Finally the prediction model is applied in forecasting the power of a HCPV power station. The results of the experiment show that this forecasting model is an efficient model.
Keywords
Markov processes; grey systems; load forecasting; neural nets; photovoltaic power systems; power engineering computing; power system reliability; HCPV power forecasting model; HCPV power station; Markov chain method; forecasting accuracy; forecasting sample data; grey neural network; power output forecasting; power system reliability; relative prediction residual error; Artificial neural networks; Forecasting; Markov processes; BP neural network; Markov chain; grey model; power forecasting;
fLanguage
English
Publisher
ieee
Conference_Titel
Cloud Computing and Intelligence Systems (CCIS), 2014 IEEE 3rd International Conference on
Print_ISBN
978-1-4799-4720-1
Type
conf
DOI
10.1109/CCIS.2014.7175760
Filename
7175760
Link To Document