Model to Generate Daily and Hourly Solar Radiation Sequences for Tropical Climates

Bao The Nguyen, Viet Van Hoang, Hiep Chi Le

Abstract


The purpose of this study is to work out a model to generate the sequences of daily and hourly solar radiation in tropical countries that are very important to calculate or simulate any solar thermal or electrical systems.  In this study, the modified Aguiar’s model is firstly used to generate daily clearness index series for Ho Chi Minh City and Da Nang, two cities presenting for two climate types in tropical region.  Then a modified model of Graham is proposed to generate hourly clearness index sequences from generate daily clearness index series for these two locations.  Two modified models prove to be more accurate in predicting daily and hourly clearness index values in comparison with Aguiar’s and Graham’s model, respectively.    Especially, the model to generate the sequences of hourly kt values proposed in this study is much simpler in comparison to the original model of Graham.  Therefore, both proposed models in this work are expected to be used to generate daily and hourly solar radiation sequences for any locations in tropical regions because they do not need to use any location-dependent parameters.


Keywords


hourly clearness index; daily clearness index; monthly average daily clearness index; hourly solar radiation sequences; daily solar radiation sequences; Markov Transition Matrix; MTM library

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References


Aguiar, R.J., Collares-Pereira, M. and Conde; J.P. (1988). Simple procedure for generating sequences of daily radiation values using a library of Markov transition matrices. Solar Energy 40, pp, 269-279.

Boland, J. (2008). Time series modeling of solar radiation. Modelling Solar Radiation at the Earth’s Surface, V. Badescu (Ed.), Springer-Verlag Berlin Heidelberg, pp. 283-312.

Brecl, K. & Topic, M. (2014). Development of a stochastic hourly solar irradiation model. International Journal of Photoenergy Volume 2014, Article ID 376504, 7 pages. http://dx.doi.org/10.1155/2014/376504.

Bright, J.M., Smith, C.J., Taylor, P.G. and Crook, R. (2015). Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data. Solar Energy 115, pp. 229-242.

Duffie, J.A. & Beckman, W.A. (2013). Solar Engineering for Thermal Processes (4th Edition). John Wiley & Sons, Inc., Hoboken, New Jersey.

Fernandez-Peruchena, C.M, Ramirez, L., Pagola, I., Gaston, M. et al. (2009). Assessment of models for estimation of hourly irradiation series from monthly mean values. 15th SolarPACES Conference, Berlin, Germany, pp.121-126. Hal-00919043.

Gafurov, T., Usaola, J. and Prodanovic, M. (2015). Incorporating spatial correlation into stochastic generation of solar radiation data. Solar Energy 115, pp. 74-84.

Hammed, W.I., Sawadi, B.A., Al-Kamil, S.I, Al-Radhi, M.S. & Abd-Alhameed, R.A. (2019). Prediction of solar irradiance based on artificial neural networks. Invention 4 (45). Doi: 10.3390/inventions40300045.

Hofmann, M., Riechelmann, S., Crisosto, C., Mubarak, R. & Seckmeyer, G. (2014). Improved synthesis of global irradiance with one-minute resolution for PV system simulations. International Journal of Photoenergy Volume 2014, Article ID 808509, 10 pages. http://dx.doi.org/10.1155/2014/808509.

HOMER Energy LLC (2020). Hybrid Optimization of Multiple Energy Resources, Ver 3.14.0. HOMER Energy, Boulder, CO, USA.

Magnano, L., Boland, J.W. and Hyndman, R.J. (2008). Generation of synthetic sequences of half hourly temperature. Environmetrics 19(8), pp. 818-835.

Mora-Lopez, L., Mora, J., Morales-Bueno, R. and Sidrach-de-Cardona, M. (2005). Modelli time series of climatic parameters with probabilistic finite automata. Environmental Modelling & Software 20, pp. 753-760.

Mora-Lopez, L. (2008). A new procedure to generate solar radiation time series from machine learning theory. Modelling Solar Radiation at the Earth’s Surface, V. Badescu (Ed.), Springer-Verlag Berlin Heidelberg, pp. 313-326.

Nguyen, T.B. and Pryor T. (1996). Generating artificial weather date sequences for Australian conditions. The 34th Annual Conference of ANZSES – Solar’96: Energy for Life, Darwin, Australia, pp. 101-108

Nguyen, T.B. and Hoang, V.V. (2017). The study of direct solar radiation data in the project of mapping the solar resource and potential in Vietnam. The 5th International Conference on Sustainable Energy, Ho Chi Minh City, Vietnam.

Priya, S.S. & Idbal, M.H. (2015). Solar radiation prediction using artificial neural networks. International Journal of Computer Applications 116 (16).

Sfetsos, A. and Coonick, A.H. (2000). Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy 68, pp. 169-178.

Soubdhan, T. & Emilion, R. (2010). Stochastic differential equation for modelling global solar radiation sequences. Modelling, Identification and Control, GOSIER, France. 10.2316/P.2010.702-099. Hal-01823269.

Tymvios, F.S., Michaelides, S, C. and Skouteli, C.S. (2008). Estimation of surface solar radiation with artificial neural networks. Modelling Solar Radiation at the Earth’s Surface, V. Badescu (Ed.), Springer-Verlag Berlin Heidelberg, pp. 221-246.

Vakili, M., Sabbagh-Yazdi, S. & Kalhor, K. (2015). Using artificial neural networks for prediction of global solar radiation in Tehran considering particulate matter air pollution. Energy Procedia 74, pp. 1205-1212.

Wu, J. & Chan, C.K. (2011). Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Solar Energy 85, pp. 808-817.




DOI (PDF): https://doi.org/10.20508/ijrer.v10i4.11454.g8056

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