TY - GEN
T1 - Temperature and Relative Humidity Prediction in Swine Livestock Buildings
AU - Molano-Jimenez, Andres
AU - Orjuela-Canon, Alvaro D.
AU - Acosta-Burbano, Wilmer
N1 - Publisher Copyright:
© 2018 IEEE.
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
UR - http://www.scopus.com/inward/citedby.url?scp=85062550286&partnerID=8YFLogxK
U2 - 10.1109/LA-CCI.2018.8625245
DO - 10.1109/LA-CCI.2018.8625245
M3 - Conference contribution
AN - SCOPUS:85062550286
T3 - 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018
BT - 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018
Y2 - 6 November 2018 through 9 November 2018
ER -