Comparing multivariate regression and artificial neural networks to model solar still production

Noe Santos, Dave James, Aly Said, Nanda Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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. Insolation, 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 distilland 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.

Original languageEnglish (US)
Title of host publication40th ASES National Solar Conference 2011, SOLAR 2011
Pages79-84
Number of pages6
StatePublished - Dec 1 2011
Event40th ASES National Solar Conference 2011, SOLAR 2011 - Raleigh, NC, United States
Duration: May 17 2011May 20 2011

Publication series

Name40th ASES National Solar Conference 2011, SOLAR 2011
Volume1

Other

Other40th ASES National Solar Conference 2011, SOLAR 2011
CountryUnited States
CityRaleigh, NC
Period5/17/115/20/11

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment

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  • Cite this

    Santos, N., James, D., Said, A., & Venkatesh, N. (2011). Comparing multivariate regression and artificial neural networks to model solar still production. In 40th ASES National Solar Conference 2011, SOLAR 2011 (pp. 79-84). (40th ASES National Solar Conference 2011, SOLAR 2011; Vol. 1).