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
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;
Conference_Titel :
Cloud Computing and Intelligence Systems (CCIS), 2014 IEEE 3rd International Conference on
Print_ISBN :
978-1-4799-4720-1
DOI :
10.1109/CCIS.2014.7175760