An adaptive neuro-fuzzy inference system (ANFIS) based prediction of solar radiation: A case study

Journal of Applied Sciences Research

Volumn 8, Issue 1, 2012, Pages 346-351

  Sumithira, T R ^a   Nirmal Kumar, A ^b   Ramesh Kumar, R ^a  

^a K S R COLLEGE OF ENGINEERING   (India)

^b Info Institute of Technology   (India)

Author keywords

Adaptive neuro fuzzy inference system; Modelling; Monthly global solar radiation; Root mean square error

Indexed keywords

EID: 84866031591     PISSN: 1816157X     EISSN: 1819544X     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. Mellit, A., A. Hadj Arab, N. Khorissi and H. Salhi, 2007. An ANFIS based forecasting for solar radiation data from sunshine duration and ambient temperature. IEEE., 1-4244-1298.
2. Meharrar, A., M. Tioursi, M. Hatti, A. Boudghène Stambouli, 2011. A variable speed wind generator maximum power tracking based on adaptive neuro-fuzzy inference system. Expert Systems with Applications, 38: 7659-7664.
3. Mika J. Arvensivua, Esko Juuso, Olli Ahava, 2001. Intelligent control of a rotary kiln fired with producer gas generated from biomass. Engineering Applications of Artificial Intelligence, 14: 629-653.
4. Angstrom, A., 1924. Solar and terrestrial radiation. Q.J.R.Meteorol.Soc., 50: 121-125.
5. Rietveld, M.R., 1978. A new method for estimating the regression coefficients in the formula relating solar radiation to sunshine. Agric. Mete., 19: 243-252.
6. Neuwirth, F., 1980. The estimation of global and sky radiation in Australia. Solar Energy, 24: 421-46.
7. Gopinathan, K.K., 1988. A general formula for computing the coefficients of the correlation connecting global solar radiation to sunshine duration. Solar Energy, 41(6): 499-502.
8. Hay, J.E., 1979. Calculation of monthly mean solar radiation for horizontal and inclined surfaces. Solar Energy, 23(4): 301-307.
9. Alawi, S.M. and H.A. Ilinai, 1988. An ANN based approach for predicting global radiation in locations with no dir e c t measurement instrumentation. Renewable Energy, 14(1-4): 199-204.
10. Kemmoku, Y., S. Orita, S. Nakagawa and T. Sakakibara, 1999. Daily insolation forecasting using a multi stage neural network. Solar Energy, 66(3): 193-199.
11. Hamdy, K.E., F.A. Faiz and S.E. Tarek, 2005. Estimation of solar radiation components incident on Helwan site using neural networks. Solar Energy, 79: 270-279.
12. Elmas, C., O. Ustun and H.H. Sayan, 2008. A neuro-fuzzy controller for speed control of a permanent magnet synchronous motor drive. Expert Systems with Applications, 34: 1.
13. Sorousha, M. and A.B. Parisa, 2009. Intelligent scenario generator for business strategic planning by using ANFIS. Expert Systems with Applications, 36: 7729-7737.
14. Jang, J., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23: 665-685.
15. Jang, J.S.R., 1991. Rule extraction using generalized neural networks. In Proceedings of the IFSA world congress (4: 82-86).
16. Avci, E., D. Hanbay and A. Varol, 2007. An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert Systems with Applications, 33(3): 582-589.
17. Aznarte, M.J.L., J.M.B. Sánchez, D.N. Lugilde, C.D.L. Fernández, C.D.Dl. Guardia and F.A. Sánchez, 2007. Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications, 32(4): 1218-1225.
18. Wang, Y.M., M.S. Taha and T.M.S. Elhag, 2008. An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications, 34(3): 3099-3106.