Hybrid experimental study evaluates thermal performance in salinity gradient solar ponds, suggesting efficient temperature forecasting using neural networks.
In the hybrid experimental study conducted, the thermal and exergy performance of a salinity gradient solar pond (SGSP) was tested under real outdoor conditions and later neural network (ANN) method was developed to predict temperatures at various depths of the pond. The SCG algorithm was used in the optimised ANN model. The highest thermal efficiency was found to be around 20.68%, and the exergy efficiency was close to 0.81%. The ANN model performed well, reaching an average R2 value above 0.998. These outcomes show that the proposed model can successfully forecast pond temperatures with high reliability.
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Özcan et al. (2026) studied this question.
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