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Prediction of the Power Output of Solar Cells Using Neural Networks: Solar Cells Energy Sector in Palestine
Ibrahim Qasrawi, Mohammed Awad
Pages - 280 - 292     |    Revised - 31-10-2015     |    Published - 30-11-2015
Volume - 9   Issue - 6    |    Publication Date - November / December 2015  Table of Contents
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KEYWORDS
Neural Networks, Solar Cell Energy, Prediction.
ABSTRACT
The prediction of the output power of solar cells in a given place has always been an important factor in planning the installation of solar cell panels, and guiding electrical companies to control, manage and distribute the energy into their electricity networks properly. The production of the electricity sector in Palestine using solar cells is a promising sector; this paper proposes a model which is used to predict future output power values of solar cells, which provides individuals and companies with future information, so they can organize their activities. We aim to create a model that able to connect time, place, and the relations between randomly distributed solar energy units. The system analyzes collected data from units through solar cells distributed in different places in Palestine. Multilayer Feed-Forward with Backpropagation Neural Networks (MFFNNBP) is used to predict the power output of the solar cells in different places in Palestine. The model depends on predicting the future produce of the power output of solar cell depending on the real power output of the previous values. The data used in this paper depends on data collection of one day, month, and year. Finally, this proposed model conduct a systematic process with the aim of determining the most suitable places for an installation solar cell panel in different places in Palestine.
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1 N. L. Panwar, S. C. Kaushik, and S. Kothari, “Role of renewable energy sources in environmental protection: a review,” Renewable and Sustainable Energy Reviews, vol. 15, no. 3, pp. 1513–1524, 2011.
2 Yaseen, Basel T. Q. “Renewable Energy Applications in Palestine”, Palestinian Energy and Environment Research Center (PEC)- Energy Authority, 2nd International Conference on the Palestinian Environment 2007.
3 Yaseen, Basel T. Q. “Renewable Energy Applications in Palestine”, Palestinian Energy and Environment Research Center (PEC)- Energy Authority, 2nd International Conference on the Palestinian Environment 2007.
4 L. Mart´in, L. F. Zarzalejo, J. Polo, A. Navarro, R.Marchante, and M. Cony, “Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning,” Solar Energy, vol. 84, no. 10, pp. 1772– 1781, 2010.
5 Duffie.J.A, and Beckman.W.A.(2006), Solar Engineering of Thermal Processes. John Wiley Sons, 3rd edition.
6 R.Ramaprabha and Dr.B.L.Mathur,”A Technique to extract maximum Power from Photovoltaic Panels”,Proc. of IEEE Int. Conf. on Recent Advancements and Applications of Computer in Electrical Engineering, pp. 447 –449, Bikaner, Rajastan, India, Mar. 24-25, 2007.
7 Engin Karatepe, Mutlu Boztepe and Metin Colak, “Development of suitable model for characterizing photovoltaic arrays with shaded solar cells”, Solar Energy, 2007, pp 329-340.
8 A. Mellit and A. M. Pavan, “A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy,” Solar Energy, vol. 84, no. 5, pp. 807–821, 2010.
9 Errachdi Ayachi, Saad Ihsen, Benrejeb Mohamed,"A Comparative Study of Nonlinear TimeVarying Process Modeling Techniques: Application to Chemical Reactor.” Journal of Intelligent Learning Systems and Applications Vol.4 No.1 (2012).
10 Ercan _Izgi a, Ahmet O¨ ztopal b, Bihter Yerli b, Mustafa Kemal Kaymak b,Ahmet Duran Sahin b,” Short–mid-term solar power prediction by using artificial Neural networks”, December 2011, Elsevier, SciVerse ScienceDirect, solar energy 86(2012) 725-733.
11 Esteban Velilla a, Jaime Valencia a, Franklin Jaramillo. Performance evaluation of two solar photovoltaic technologies under atmospheric exposure using artificial neural network models, Elsevier, solar energy [2014].
12 Ayman Abualkhair,” Electricity sector in the Palestinian territories: Which priorities for Development and peace?” Elsevier, Energy Policy 35 (2007) 2209–2230.
13 Bader M. Alluhaidah, “MOST INFLUENTIAL VARIABLES FOR SOLAR RADIATION FORECASTING USING ARTIFICIAL NEURAL NETWORKS”, Dalhousie University Halifax, Nova Scotia [June 2014].
14 Aminmohammad Saberian, H. Hizam, M. A. M. Radzi,M. Z. A. Ab Kadir, andMaryam Mirzaei, “Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks”, Hindawi Publishing Corporation International Journal of PhotoenergyVolume 2014, Article ID 469701, 10 pages, [April 2014].
15 N. S. Shrirao ,Mr D. H. Bodkhey, Mr. Sapan Kumar Singh. “A Review of Sensor Networks: Challenges and solutions “, International Journal of ICT and Management, February 2013 Vol- I Issue –I.
16 K.Hornik, M. Stinchcombe, and H.White, “Multilayer feedforwardnetworks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989.
17 M. T. Hagan, H. B. Demuth, and M. Beale, Neural Network Design, PWS Publishing Company, Boston,Mass, USA, 1995.
18 Simon S. Haykin, (2008), Neural Networks-A Comprehensive Foundation, 2nd Edition. ISBN13: 978-0132733502.
19 K. Madsen, H.B. Nielsen, and O. Tingleff. Methods for Non-Linear Least Squares Problems. T echnical University of Denmark, 2004. Lecture notes.
20 levmar- Manolis I. A. Lourakis, [February 11, 2005],” A Brief Description of the LevenbergMarquardt Algorithm Implemented”, Institute of Computer Science-Foundation for Research and Technology - Hellas (FOR TH)V assilika V outon, P .O. Box 1385, GR 711 10 Heraklion, Crete, GREECE.
Mr. Ibrahim Qasrawi
aauj /Computer science - Palestinian Occupied Territori
iqasrawi@outlook.com
Dr. Mohammed Awad
aauj /Computer engineering - Palestinian Occupied Territori