Missions to Mars need a power source, while, one of the most compatible sources for such a purpose is the photovoltaic system. Photovoltaic systems generate power based on the available energy from the Sun, and thus, solar radiation intensity at Mars should be known for design purposes. In this research, the feed-forward back-propagation artificial neural network is developed to predict solar radiation in terms of longitude, latitude, time of the day, temperature, altitude, pressure, amount of dust, and volume mixing ratio of water ice clouds. Data which are used to develop this model are obtained from the Mars Climate Database. The results of the developed method are accurate as compared with other methods whereas the correlation (R2) coefficient for the developed model is 0.97. The developed model then is used to predict mean solar radiation and mean temperature for every location on Mars and then the data are presented on Mars maps in order to determine the best location for harvesting energy from the Sun by photovoltaic systems. According to results, the solar radiation-temperature belt on Mars is found to be between latitudes 20 deg south and 15 deg north.
Determination of Mars Solar-Belt by Modeling of Solar Radiation Using Artificial Neural Networks
Contributed by the Solar Energy Division of ASME for publication in the Journal of Solar Energy Engineering: Including Wind Energy and Building Energy Conservation. Manuscript received November 17, 2018; final manuscript received June 28, 2019; published online July 31, 2019. Assoc. Editor: Dr. Gerardo Diaz.
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Khatib, T., Abunajeeb, I., and Heneni, Z. (July 31, 2019). "Determination of Mars Solar-Belt by Modeling of Solar Radiation Using Artificial Neural Networks." ASME. J. Sol. Energy Eng. February 2020; 142(1): 011007. doi: https://doi.org/10.1115/1.4044304
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