Solar irradiance forecasting based on long-wave atmospheric radiation

• Autorzy: Piątek M. , Trajer J. , Czekalski D.
• Języki publikacji: EN

Abstrakty

EN

This work contains information concerning long-wave atmospheric radiation. Artificial neural networks were developed to forecast total mean hourly irradiance based on long-wave atmospheric radiation as cloudiness indicator. It was proved that using this variable in models for forecasting irradiance is wellgrounded. The proof was based on the neural networks sensitivity analysis. It was proved that neural network model is capable to utilize information carried by long wave atmospheric radiation only when the air temperature is provided as additional explanatory variable.

Słowa kluczowe

EN

artificial neural networks; irradiance forecasting; cloudiness

• Wydawca: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego w Olsztynie
• Czasopismo: Technical Sciences / University of Warmia and Mazury in Olsztyn
• Rocznik: 2015
• Tom: nr 18(1)
• Strony: 27--36
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Piątek M.

• Faculty of Production Engineering, SGGW in Warsaw

autor

Trajer J.

• Faculty of Production Engineering, SGGW in Warsaw

autor

Czekalski D.

• Faculty of Production Engineering, SGGW in Warsaw

Bibliografia

• BEHRANG M.A., ASSAREH E., GHANBARZADEH A., NOGHREHABADI A.R. 2010. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy, 84: 1468-1480.
• ESCRIG H., BATLLES F.J., ALONSO J., BAENA F.M., BOSCH J.L., SALBIDEGOITIA I.B., BURGALETA J.I. 2013. Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast. Energy, 55: 853-859. http://dx.doi.org/10.1016/j.energy.2013.01.054.
• FURLAN C., OLIVEIRA A.P., JACYRA SOARES J., CODATO G., ESCOBEDO J.F. 2012. The role of clouds in improving the regression model for hourly values of diffuse solar radiation. Applied Energy, 92: 240-254.
• JIANG Y. 2008. Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models. Energy Policy, 36: 3833-3837.
• KJAERSGAARD J.H., PLAUBORG F.L., HANSEN S. 2007. Comparison of models for calculating daytime long-wave irradiance using long term data set. Agricultural and Forest Meteorology, 143: 49-63.
• LHOMME J.P., VACHER J.J., ROCHETEAU A. 2007. Estimating downward long-wave radiation on the Andean Altiplano. Agricultural and Forest Meteorology, 145: 139-148.
• MUBIRU J., BANDA E.J.K.B. 2008. Estimation of monthly average daily global solar irradiation using artificial neural networks. Solar Energy, 82: 181-187.
• SOARES J., OLIVEIRA A.P., BOZNAR M.Z., MLAKAR P., ESCOBEDO J., MACHADO A. 2004. Modeling hourly diffuse solar-radiation in the city of Sao Paulo using a neural-network technique. Applied Energy, 79: 201-214.
• TRAJER J., CZEKALSKI D. 2005. Prognozowanie sum napromienienia słonecznego dla potrzeb energetyki słonecznej. Inżynieria Rolnicza, 8(68): 393-399.
• TRAJER J., KOZŁOWSKI K. 2005. Neuronowy model prognozowania dziennego napromienienia słonecznego. Inżynieria Rolnicza, 14(74): 361-366.
• WITTEN I.H., FRANK E. 2000. Data Mining: Practical Machine Learning Tools and Techniques. New York: Morgan Kaufmann.
• YADAV A.K., CHANDEL S.S. 2013. Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and Sustainable Energy Reviews, 33: 772-781. http://dx.doi.org/10.1016/j.rser.2013.08.055.