Deep learning approaches for predicting solar radiation and freshwater yield in modified pyramid solar still

Water deficiency is a significant globally challenge that requires the advancement of sustainable and effective desalination methods. Solar stills provide a feasible solution for the production of fresh water in areas dealing with water limitations, particularly in remote locations. However, their efficacy is frequently limited by fluctuating climatic conditions. The intermittent and changing character of solar radiation imposes significant limitations on most applications. Accurate solar radiation forecasting is crucial for estimating the distillate yield of a solar still system. With computer technology developing so quickly, a growing many deep learning models are employed in solar radiation prediction. For this purpose, the article evaluates the freshwater yield of the modified pyramid solar still in Tehran and Zahedan, Iran. Utilizing monthly data from 1984 to 2023 and employing Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and CNN-LSTM algorithms, predictions for solar irradiance and temperature are calculated for the next ten years. The results validated the better performance of the CNN and GRU models for Tehran, while the LSTM model succeeded for Zahedan in forecasting global solar irradiance (GHI) and temperature. The presented models are utilized for predict the monthly output of the studied solar stills. The predicted average annual freshwater yield for the ten years from 2024 to 2033 is calculated to be 2630 L in Tehran and 2710 L in Zahedan.