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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
Neural Networks, Solar Cell Energy, Prediction.
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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Mr. Ibrahim Qasrawi
aauj /Computer science - Palestinian Occupied Territori
Dr. Mohammed Awad
aauj /Computer engineering - Palestinian Occupied Territori