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 language | English (US) |
|---|---|
| Title of host publication | 40th ASES National Solar Conference 2011, SOLAR 2011 |
| Pages | 79-84 |
| Number of pages | 6 |
| Volume | 1 |
| State | Published - Dec 1 2011 |
| Event | 40th ASES National Solar Conference 2011, SOLAR 2011 - Raleigh, NC, United States Duration: May 17 2011 → May 20 2011 |
Other
| Other | 40th ASES National Solar Conference 2011, SOLAR 2011 |
|---|---|
| Country | United States |
| City | Raleigh, NC |
| Period | 5/17/11 → 5/20/11 |
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All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
Cite this
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Comparing multivariate regression and artificial neural networks to model solar still production. / Santos, Noe; James, Dave; Said, Aly Marei; Venkatesh, Nanda.
40th ASES National Solar Conference 2011, SOLAR 2011. Vol. 1 2011. p. 79-84.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Comparing multivariate regression and artificial neural networks to model solar still production
AU - Santos, Noe
AU - James, Dave
AU - Said, Aly Marei
AU - Venkatesh, Nanda
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84867003882&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867003882&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781618394279
VL - 1
SP - 79
EP - 84
BT - 40th ASES National Solar Conference 2011, SOLAR 2011
ER -