Estimating the Annual Global Solar Radiation In Three Jordanian Cities by Using Air Temperature Data

I. M.  Abolgasem

doi:10.51646/jsesd.v5i2.61

المؤلفون

I. M. Abolgasem

DOI:

https://doi.org/10.51646/jsesd.v5i2.61

الكلمات المفتاحية:

artifiial neural network ، global solar radiation ، modeling energy systems ، algorithm

الملخص

Estimating solar radiation is an imperative requirement for solar energy development in Jordan. In this paper, a quantitative approach, based on Artificial Neural Network, was developed for estimating the annual global solar radiation of three Jordanian cities: Amman, Irbid and Aqaba. These cities are currently witnessing huge development and increasing demand for energy supply. Using a set of known meteorological parameters, two Artificial Neural Network (ANN) models with different architectures, called case 1 and case 2, fed with three types of learning algorithms for data training and testing, were designed to identify
the optimum conditions for obtaining reliable and accurate prediction of the solar radiation. The results showed that model case 1 performed generally better in terms of predicting the annual GSR (96%) compared to model case 2 (95%). Furthermore, the algorithms LM and SCG, in general, ensured the highest efficiency in training and testing the data in the designed models compared to the GDX algorithm. Therefore, model case 1, designed with one of these two algorithms, is selected as the optimal model design that is able to compute with high accuracy the annual solar radiation for the three studied cities.

التنزيلات

بيانات التنزيل غير متوفرة بعد.

المقاييس

يتم تحميل المقاييس...

المراجع

. Zafar, S. 2012. Solar Energy in Jordan (online article). Accessed on 12 Dec 2013. Available on World Wide Web: www.ecomena.org/solarenergy-jordan

. NERC. 2005. Report of National Energy Research Center, Ministry of Energy and Mineral Resources, Amman, Jordan.

. Hrayshat, E.S. and Al-Soud, M.S. 2004. Solar energy in Jordan: current state and prospects. Renewable and Sustainable Energy Reviews, 8(2): 193–200

. IEA. 2011. Online report of IEA Key energy statistics 2011 Page: Country-specific indicator numbers. Accessed on 12 Dec 2013. Available on World Wide Web: www.iea.org/textbase/nppdf/ free/2011/key_world_energy_stats.pdf.

. Lillesand, T.M. & Kiefer, R.W. 2000. Remote Sensing and Image Interpretation.4thEdition. John Wiley & Sons, Inc. New York. pp. 589-592

. López, G., Martínez, M., Rubio, M.A. &Batlles, F.J. 2001. Estimation of hourly global photosynthetically active radiation using artificial neural networks.Agricultural and Forest Meteorology, 107: 279–91.

. Cybenko, G. 1989. Approximation by superposition of a sigmoidal function. Mathematics of Control Signal and Systems, 2: 303–14.

. Haykin, S. 2009. Neural Networks and Learning Machines.3rd Edition.Pearson Education, Inc, New Jersey.

. Jiang, Y. 2009. Computation of monthly mean daily global solar radiation in China using artifiial neural networks and comparison with other empirical models. Energy, 34: 1276–1283.

. Tadros, M.T.Y. 2000. Uses of sunshine duration to estimate the global solar radiation over eight meteorological stations in Egypt. Renewable Energy, 21(2): 231–46.

. Bakirci, K. 2009. Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey.Energy, 34(4): 485–501.

. Almorox, J. &Hontoria, C. 2004. Global solar radiation estimation using sunshine duration in Spain.Energy Conversion and Management, 45(9–10): 1529–1535.

. Kalogirou, S., Michaelides, S. &Tymvios, F. 2002. Prediction of maximum solar radiation using artificial neural networks.World Renewable Energy Congress VII.

. Rehman, S. &Mohandes, M. 2008. Artifiial neural network estimation of global solar radiation using air Temperature and relative humidity.Energy Policy 36: 571-576.

. Tasadduq, I., Rehman, S. &Bubshait, K. 2002. Application of neural networks for the prediction of hourly mean surface temperature in Saudi Arabia, Renewable Energy, 25: 545– 554.

. Mishraa, A., Kaushikaa, N,D . Zhangb, G and Zhoub,J. 2008. Artificial neural network model for the estimation of direct solar radiation in the Indian zone.International Journal of Sustainable Energy,27 (3) 95–103.

. Meza,F., Varas,E. 2000. Estimation of mean monthly solar global radiation as a function of temperature. Agricultural and Forest Meteorology, 100: 231–241.

. Morid, S., Gosainb, A. K. & Keshari, A. K. 2002. Solar Radiation Estimation using Temperaturebased, Stochastic and Artifiial Neural Networks Approaches. Nordic Hydrology,33(4) 29: 1-304.