DocumentCode :
1879538
Title :
Solar power forecasting modeling using soft computing approach
Author :
Singh, V.P. ; Vaibhav, K. ; Chaturvedi, D.K.
Author_Institution :
Indian Inst. of Technol. Rajasthan, Jodhpur, India
fYear :
2012
fDate :
6-8 Dec. 2012
Firstpage :
1
Lastpage :
5
Abstract :
In last few years, Renewable Energy is introduced as a alternative source of energy. Especially in Indian context solar Energy is an important issue and unlimited source of energy. However, solar radiation is varies with time and geographical locations and meteorological conditions. In this paper, artificial neural network and generalized neural network are used as a powerful tool for Renewable Energy Forecasting. With the help of metrological data such as wind velocity, solar irradiation, and temperature as input to the model we can predict the changes in generated solar power, which is very useful for integration of solar power into grid. In this paper these soft computing techniques are able to prediction the solar power generation accurately and fast compare to conventional methods of forecasting.
Keywords :
neural nets; power engineering computing; power generation planning; power grids; solar power stations; artificial neural network; generalized neural network; metrological data; power grid; soft computing approach; solar irradiation; solar power forecasting modeling; solar power integration; solar radiation; temperature input; wind velocity; artificial neural network and generalized neural network; forecasting; soar power;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering (NUiCONE), 2012 Nirma University International Conference on
Conference_Location :
Ahmedabad
Print_ISBN :
978-1-4673-1720-7
Type :
conf
DOI :
10.1109/NUICONE.2012.6493268
Filename :
6493268
Link To Document :
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