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

Noe Santos; Dave James; Aly Said; Nanda Venkatesh

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

Noe Santos
, Dave James
, Aly Said
, Nanda Venkatesh

Architectural Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	40th ASES National Solar Conference 2011, SOLAR 2011
Pages	79-84
Number of pages	6
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

Publication series

Name	40th ASES National Solar Conference 2011, SOLAR 2011
Volume	1

Other

Other	40th ASES National Solar Conference 2011, SOLAR 2011
Country/Territory	United States
City	Raleigh, NC
Period	5/17/11 → 5/20/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Renewable Energy, Sustainability and the Environment

Cite this

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title = "Comparing multivariate regression and artificial neural networks to model solar still production",

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.",

author = "Noe Santos and Dave James and Aly Said and Nanda Venkatesh",

year = "2011",

month = dec,

day = "1",

language = "English (US)",

isbn = "9781618394279",

series = "40th ASES National Solar Conference 2011, SOLAR 2011",

pages = "79--84",

booktitle = "40th ASES National Solar Conference 2011, SOLAR 2011",

note = "40th ASES National Solar Conference 2011, SOLAR 2011 ; Conference date: 17-05-2011 Through 20-05-2011",

}

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T1 - Comparing multivariate regression and artificial neural networks to model solar still production

AU - Santos, Noe

AU - James, Dave

AU - Said, Aly

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 - https://www.scopus.com/pages/publications/84867003882

UR - https://www.scopus.com/pages/publications/84867003882#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:84867003882

SN - 9781618394279

T3 - 40th ASES National Solar Conference 2011, SOLAR 2011

SP - 79

EP - 84

BT - 40th ASES National Solar Conference 2011, SOLAR 2011

T2 - 40th ASES National Solar Conference 2011, SOLAR 2011

Y2 - 17 May 2011 through 20 May 2011

ER -

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

Abstract

Publication series

Other

UN SDGs

All Science Journal Classification (ASJC) codes

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