Abstract
The increasing demand for renewable and environmentally friendly energy sources is a top priority for many countries around the world. It is obvious that renewable solar energy will help to meet most of the energy demand in the coming years. A solar pond is a huge Salt artificial Lake that serves as a solar energy collection system. However, site selection is a critical factor that affects the effectiveness and lifetime of a solar pond. Here, we present an innovative methodology for site selection based on three environmental factors, including direct solar irradiance (DNI), temperature, and wind speed. Our approach uses Python programming and clustering analysis with several libraries, including Pandas, Geopandas, Rasterio, Osgeo, and Sklearn, to analyse and process data collected over a 30-year period from NASA power. This method was applied to the geographic boundaries of Egypt, but the methods can be applied to any spatial context if the same dataset is available. The results show that Egypt has a potential land area of 500 km2 suitable for solar ponds construction along the border with Sudan throughout the year, including 2000 km2 in winter (between January and March), 800 km2 in spring (between April and June), 900 km2 in summer (between July and September), and the largest area of 3700 km2 (between October and December), most of which is located in the south of the Eastern Desert and around the Nile River. Notably, the northwestern region, close to the Mediterranean Sea on the border with Libya, exhibits suitability for solar pond development, with consistent performance throughout the year. Our results provide an efficient way for GIS and data processing and could be useful for implementing new software to find the best location for solar ponds development. This could be beneficial for those interested in investing in renewable energy and using solar ponds as an efficient water treatment plant.
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