Abstract
Solar radiation data is needed by engineers, architects and scientists in the framework of studies on photovoltaic or thermal solar systems. A stochastic model for simulating global solar radiation is useful in reliable power systems calculations. The main objective of this paper is to present an algorithm to predict hourly solar radiation in the short/medium term, combining information about cloud coverage level and historical solar radiation registers, which increased the performance and the accuracy of the forecasting model. The use of Artificial Neural Networks (ANN) model is an efficient method to forecast solar radiation during cloudy days by one day ahead. The results of three statistical indicators - Mean Bias Error (MBE), Root Mean Square Error (RMSE), and t-statistic (TS) - performed with estimated and observed data, validate the good performance accuracy of the proposed three indicators.
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References
Stull, R.B.: Meteorology Today For Scientists and Engineers: A Technical Companion Book. West Publishing Company, Minneapolis/St. Paul (1995)
Graham, V., Hollands, K.: A method to generate synthetic hourly solar radiation globally. Int. J. Solar Energy 44, 333–341 (1990)
Badescu, V.: Modelling Solar radiation at the earth surface. Springer (2008) ISBN 978-3-540-77454-9
Khatib, T., Mohamed, A., Sopian, K.: A review of solar energy modeling techniques. Renewable and Sustainable Energy Reviews 16, 2864–2869 (2012)
Batlles, F.J., Rubio, M.A., Tovar, J., Olmo, F.J., Alados-Arboledas, L.: Empirical modeling of hourly direct irradiance by means of hourly global irradiance. Energy 25, 675–688 (2000)
Aguiar, R., Collares-Pereira, M.: A time dependent autoregressive Gaussian model for generating synthetic hourly radiation. Solar Energy 49, 167–174 (1992)
Maafi, A.A.A.: A two state Markovian model of global irradiation suitable for photovoltaic conversion. Solar and Wind Technology 6, 247–252 (1989)
Mellit, A., Benghanemb, M., Hadj Arabc, A., Guessoumd, A.: A simplified model for generating sequences of global solar radiation data for isolated sites: using artificial neural network and a library of Markov transition matrices approach. Solar Energy 79, 469–482 (2005)
Cao, J.C., Cao, S.H.: Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy 31, 3435–3445 (2006)
Capizzi, G., Napoli, C., Bonanno, F.: Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting. IEEE Trans. on Neural Networks and Learning Syst. 23(11), 1805–1815 (2012)
Jones, P.: Cloud-cover distribution and correlations. J. Appl. Meterol. 31, 732–741 (1992)
Hourly solar radiation, http://snirh.pt/index.php?idMain=2&idItem=1
Paulescu, M., et al.: Weather Modeling and Forecasting of PV Systems Operation. Green Energy and Technology, pp. 17–42 (2013), doi:10.1007/978-1-4471-4649-0_2
Aguiar, R., Collares-Pereira, M.: TAG: A time-dependent, autoregressive, Gaussian model for generating synthetic hourly radiation. Solar Energy 49(3), 167–174 (1992)
Kandilli, C., Ulgen, K.: Solar Illumination and Estimating Daylight Availability of Global Solar Irradiance. Energy Sources
Ahmad, M.J., Tiwari, G.N.: Solar radiation models – review. International Journal of Energy and Environment 1(3), 513–532 (2010)
Bakirci, K.: Models of solar radiation with hours of bright sunshine: A review. Renewable and Sustainable Energy Reviews 13, 2580–2588 (2009)
Myers, D.R.: Solar radiation modeling and measurements for renewable energy applications: data and model quality. Energy 30, 1517–1531 (2005)
Muneer, T., Younes, S., Munawwar, S.: Discourses on solar radiation modeling. Renewable and Sustainable Energy Reviews 11, 551–602 (2007)
Stone, R.J.: Improved statistical procedure for the evaluation of solar radiation estimation models. Solar Energy 51(4), 289–291 (1993)
Ferreira, P.M., Gomes, J.M., Martins, I.A.C., Ruano, A.E.: A Neural Network based Intelligent Predictive Sensor for Cloudiness, Solar Radiation, and Air Temperature. Sensors 12, 15750–15777 (2012)
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Faceira, J., Afonso, P., Salgado, P. (2015). Prediction of Solar Radiation Using Artificial Neural Networks. In: Moreira, A., Matos, A., Veiga, G. (eds) CONTROLO’2014 – Proceedings of the 11th Portuguese Conference on Automatic Control. Lecture Notes in Electrical Engineering, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-10380-8_38
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DOI: https://doi.org/10.1007/978-3-319-10380-8_38
Publisher Name: Springer, Cham
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