Title :
Comparison of different artificial neural network techniques in prediction of solar radiation for power generation using different combinations of meterological variables
Author :
Yadav, Amit Kumar ; Malik, Hasmat
Author_Institution :
Centre for Energy & Environ., Nat. Inst. of Technol., Hamirpur, India
Abstract :
The main objective of present study is to compare ANN model develop with neural network fitting tool (nftool), Radial Basis Function Neural Network (RBFNN) in predicting solar radiation for power generation. The three combinations of input variables are considered for prediction. The RBFNN utilizing input parameters as latitude, longitude, height above sea level and sunshine hours has mean absolute percentage error (MAPE) of 4.94% and absolute fraction of variance (R2) of 96.18% respectively and it give better results than conventional solar radiation prediction models (Angstrom, Akinoglu and Ecevit, Bahel, Almorox and Hontoria). Therefore RBFNN can be used for prediction of solar radiation for solar power generation.
Keywords :
power engineering computing; radial basis function networks; solar power stations; sunlight; ANN model; Akinoglu-Ecevit solar radiation prediction model; Almorox solar radiation prediction model; Angstrom solar radiation prediction model; Bahel solar radiation prediction model; Hontoria solar radiation prediction model; MAPE; RBFNN; absolute fraction-of-variance; artificial neural network technique; height-above-sea level; latitude; longitude; mean absolute percentage error; meterological variable combination; neural network fitting tool; nftool; radial basis function neural network; solar power generation; sunshine hours; Artificial neural networks; Cities and towns; Ocean temperature; Predictive models; Sea level; Solar radiation; Training; ANN; RBFNN; conventional models; solar radiation;
Conference_Titel :
Power Electronics, Drives and Energy Systems (PEDES), 2014 IEEE International Conference on
Conference_Location :
Mumbai
Print_ISBN :
978-1-4799-6372-0
DOI :
10.1109/PEDES.2014.7042063