Temperature and Relative Humidity Prediction in Swine Livestock Buildings

Andres Molano-Jimenez; Alvaro D. Orjuela-Canon; Wilmer Acosta-Burbano

doi:10.1109/LA-CCI.2018.8625245

Temperature and Relative Humidity Prediction in Swine Livestock Buildings

Andres Molano-Jimenez, Alvaro D. Orjuela-Canon, Wilmer Acosta-Burbano

Research output: Contribution to conference › Conference proceedings › peer-review

2 Scopus citations

Abstract

Based on available data from a swine livestock warehouse located in Puerto Gaitan - Meta, four models were proposed to predict relative humidity and temperature using artificial neural networks and measurements from temperature, humidity and CO₂ concentration. Results seem to indicate that the model structures used are suitable for predict humidity in barns not equipped with humidity sensors and improve current installed microclimate control systems in Colombia.

Original language	English (US)
DOIs	https://doi.org/10.1109/LA-CCI.2018.8625245
State	Published - Jan 23 2019
Externally published	Yes
Event	2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018 - Gudalajara, Mexico Duration: Nov 6 2018 → Nov 9 2018

Conference

Conference	2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018
Country/Territory	Mexico
City	Gudalajara
Period	11/6/18 → 11/9/18

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Information Systems and Management

UN SDGs

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

Access to Document

10.1109/LA-CCI.2018.8625245

Cite this

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title = "Temperature and Relative Humidity Prediction in Swine Livestock Buildings",

abstract = "Based on available data from a swine livestock warehouse located in Puerto Gaitan - Meta, four models were proposed to predict relative humidity and temperature using artificial neural networks and measurements from temperature, humidity and CO2 concentration. Results seem to indicate that the model structures used are suitable for predict humidity in barns not equipped with humidity sensors and improve current installed microclimate control systems in Colombia.",

author = "Andres Molano-Jimenez and Orjuela-Canon, {Alvaro D.} and Wilmer Acosta-Burbano",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018 ; Conference date: 06-11-2018 Through 09-11-2018",

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AU - Molano-Jimenez, Andres

AU - Orjuela-Canon, Alvaro D.

AU - Acosta-Burbano, Wilmer

PY - 2019/1/23

Y1 - 2019/1/23

N2 - Based on available data from a swine livestock warehouse located in Puerto Gaitan - Meta, four models were proposed to predict relative humidity and temperature using artificial neural networks and measurements from temperature, humidity and CO2 concentration. Results seem to indicate that the model structures used are suitable for predict humidity in barns not equipped with humidity sensors and improve current installed microclimate control systems in Colombia.

AB - Based on available data from a swine livestock warehouse located in Puerto Gaitan - Meta, four models were proposed to predict relative humidity and temperature using artificial neural networks and measurements from temperature, humidity and CO2 concentration. Results seem to indicate that the model structures used are suitable for predict humidity in barns not equipped with humidity sensors and improve current installed microclimate control systems in Colombia.

UR - http://www.scopus.com/inward/record.url?scp=85062550286&partnerID=8YFLogxK

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U2 - 10.1109/LA-CCI.2018.8625245

DO - 10.1109/LA-CCI.2018.8625245

M3 - Conference proceedings

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Y2 - 6 November 2018 through 9 November 2018

ER -

Temperature and Relative Humidity Prediction in Swine Livestock Buildings

Abstract

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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