Productivity forecasting of the solar still with phase change material using LSTM and GRU neural networks

Abstract Solar desalination is one of the renewable energy techniques by which freshwater can be obtained economically. Solar desalination experiments are time and resource-consuming methods; hence there is a need for a robust system to identify the serviceability of the solar still in a specific region. The objective of the present study is to develop a forecasting model using artificial neural networks to predict freshwater productivity. Specifically, the study aims to compare the accuracy of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks in forecasting the productivity of Conventional Solar Stills (CSS) and Solar Stills with Phase Change Material (SSPCM). Additionally, the research seeks to analyze the impact of advanced materials, such as phase change materials (PCM) and pin fins, on solar still productivity; determine which neural network model provides more accurate and reliable forecasts for different solar still designs; and contribute to the optimization of solar still implementations by developing predictive models applicable to diverse geographical regions. The current investigation involved analysing the experimental outcomes of a solar still that employed phase change material (PCM) and pin fins. Palmitic acid was implemented as the energy storage material and was placed beneath the absorber plate. The neural network model was trained and validated using time-series solar still experimental data. Different statistical measures were utilised to evaluate the accuracy of Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results indicate that the freshwater productivity forecasted by LSTM exhibited greater accuracy than GRU. Specifically, the coefficient of determination values for LSTM were 0.96 and 0.98 for the CSS and SSPCM, respectively, which were higher than the corresponding values for GRU.