Freshwater yield prediction from modified solar still: An analysis of deep learning models for forecasting in Tehran

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. The intermittent and changing character of solar radiation imposes significant limitations on most applications. The accurate forecasting of solar radiation is crucial for estimating the distillate yield of a solar still system. For this purpose, the study evaluates the freshwater yield of the modified pyramid solar still in Tehran. Utilizing monthly data from ۱۹۸۴ to ۲۰۲۳ 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 in forecasting solar radiation and temperature. The predicted average annual freshwater yield for the ten years from ۲۰۲۴ to ۲۰۳۳ is calculated to be ۲۶۳۰ liters in Tehran.

کلیدواژه ها:

Pyramidal solar still ، Productivity Forecasting ، Solar radiation ، Deep learning ، Long-term prediction