Performance Prediction of solar still using Artificial neural network. (2023)

• Record Type: Journal Article
• Authors: Immanual, R.; Kannan, K.; Chokkalingam, B.; Priyadharshini, B.; Sathya, J.; Sudharsan, S.; Raghu Nath, E.
• Is Part Of: Materials today. Volume 72(2023)Part 1
• Page Start: 430
• Page End: 440
• Publication Date: 2023
• Subjects: Solar still -- Artificial neural networks (ANNs) -- Yield -- Solar energy -- Intelligent -- Forecast; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.08.311
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The development of various solar energy systems has emerged as one of the most important answers to the problem of rising energy demand. By utilising the notion of Intelligent system-based methodologies to forecast the performance of solar stills working in an air-dry environment. The artificial neural network concept was applied to forecast the performance of single slope single basin solar stills in this study.Data gathered from previously published articles and used it to forecast the performance of conventional solar still. The developed model can be used to forecast productivity with accuracy of 0.99459 by using LM algorithm.