DocumentCode :
498608
Title :
Modeling climate parameters for renewable energy applications in the UAE using neural networks
Author :
El Chaar, L. ; Lamont, L.A. ; Karkoub, M.
Author_Institution :
Pet. Inst., United Arab Emirates
fYear :
2009
fDate :
29-31 July 2009
Firstpage :
1
Lastpage :
10
Abstract :
This paper aims to create prediction models for both global solar radiation and wind speed for the city of Abu Dhabi in the United Arab Emirates. To do so neural network techniques using feed-forward back propagation were deployed and samples for the month of January for such models are presented. The results confirm the accuracy of the models and compare the measured output with the neural network trained outputs. Such models will then be used for estimating power generation using photovoltaics and/or wind turbines.
Keywords :
backpropagation; neural nets; power engineering computing; solar power stations; wind power plants; climate parameter modelling; feedforward back propagation; neural network techniques; photovoltaic power generation; power generation; power generation estimation; prediction model; renewable energy application; solar radiation; wind speed; wind turbines; Cities and towns; Feedforward neural networks; Feedforward systems; Neural networks; Predictive models; Renewable energy resources; Solar radiation; Wind energy generation; Wind power generation; Wind speed; Empirical Models; Feedforward Neural Network; Meteorological; Neural Networks; Prediction Methods; Solar Energy; Solar Radiation; Wind Energy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Integration of Wide-Scale Renewable Resources Into the Power Delivery System, 2009 CIGRE/IEEE PES Joint Symposium
Conference_Location :
Calgary, AB
Print_ISBN :
978-1-4244-4860-9
Electronic_ISBN :
978-2-85873-080-3
Type :
conf
Filename :
5211184
Link To Document :
بازگشت