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

Articles in Press

Document Type : Full Length Research Article

Authors

1 School of Mechanical Engineering, Iran University of Science and Technology, Tehran

2 School of New Technologies, Iran University of Science and Technology, Tehran

Abstract

Water deficiency is a significant global 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 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 in forecasting solar radiation and temperature. The predicted average annual freshwater yield for the ten years from 2024 to 2033 is calculated to be 2630 liters in Tehran. These findings emphasize the importance of integrating accurate solar forecasting techniques with renewable desalination systems to optimize water production. Furthermore, the approach outlined in this study can be applied to other regions with similar climatic conditions to enhance freshwater accessibility and ensure long-term water sustainability.

Keywords

Main Subjects

Journal of Heat and Mass Transfer Research

Articles in Press, Accepted Manuscript
Available Online from 29 November 2025

Files

History

  • Receive Date: 16 September 2025
  • Revise Date: 19 October 2025
  • Accept Date: 29 November 2025

Share

How to cite

Statistics