Comparing Multivariate Regression and Artificial Neural Networks to Model Solar Still Production
A study has been performed to predict solar still performance using data originally gathered between February 2006 and August 2007. The purpose of this study was to determine the viability of modeling distillate production using local weather data with artificial neural networks (ANNs) and multivariate regression (MVR). This study used weather variables which were hypothesized to affect still performance. Insulation, wind velocity, wind direction, cloud cover, and ambient temperature were the main weather variables that were used as the input data along with the operating distill and volume. The objectives of this study were to determine the minimum amount of inputs required to accurately model solar still performance and to examine which type of model performed the best.
Artificial intelligence; Clouds; Distillation; Distilled water; Neural networks (Computer science); Regression analysis; Solar stills; Temperature; Waterl; Water--Purification--Distillation process; Weather; Winds
Civil and Environmental Engineering | Environmental Engineering | Environmental Sciences | Natural Resources and Conservation | Sustainability | Water Resource Management
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Santos, N. I.,
Said, A. M.,
James, D. E.,
Venkatesh, N. H.
Comparing Multivariate Regression and Artificial Neural Networks to Model Solar Still Production.
Proceedings of the 40th American Solar Energy Society National Conference
American Solar Energy Society.