TY - JOUR
T1 - Model predictive temperature and humidity control of greenhouse with ventilation
AU - Ito, Kazuhisa
AU - Tabei, Tsubasa
N1 - Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.
PY - 2021
Y1 - 2021
N2 - Facility gardening with complex environmental control systems has potential in improving productivity and producing high value-added agricultural products; however, its introduction is expensive. Thus, this study proposes a greenhouse temperature and humidity control system using relatively inexpensive heaters, humidifiers, and ventilation fans. Humidity control is expected to promote photosynthesis and prevent plant disease. The concept of model predictive control (MPC) was applied to a small greenhouse to minimise the sum of squared errors. Compared with the two conventional methods, the simulation results showed that MPC reduced the relative RMS error of the temperature and humidity deficit to 23.5% and 13.1%, respectively.
AB - Facility gardening with complex environmental control systems has potential in improving productivity and producing high value-added agricultural products; however, its introduction is expensive. Thus, this study proposes a greenhouse temperature and humidity control system using relatively inexpensive heaters, humidifiers, and ventilation fans. Humidity control is expected to promote photosynthesis and prevent plant disease. The concept of model predictive control (MPC) was applied to a small greenhouse to minimise the sum of squared errors. Compared with the two conventional methods, the simulation results showed that MPC reduced the relative RMS error of the temperature and humidity deficit to 23.5% and 13.1%, respectively.
KW - Greenhouse
KW - Humidity deficit
KW - Model predictive control
KW - Temperature
KW - Ventilation
UR - http://www.scopus.com/inward/record.url?scp=85116878636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85116878636&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2021.08.022
DO - 10.1016/j.procs.2021.08.022
M3 - Conference article
AN - SCOPUS:85116878636
SN - 1877-0509
VL - 192
SP - 2
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021
Y2 - 8 September 2021 through 10 September 2021
ER -