Submit Manuscript

International Journal of Agricultural Research

Year: 2010 | Volume: 5 | Issue: 4 | Page No.: 191-200
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Article Trend

Total views 330

Search

Authors

R. Salazar

U. Schmidt

C. Huber

A. Rojano

I. Lopez

Keywords

Research Article

Neural Networks Models for Temperature and CO2 Control

R. Salazar, U. Schmidt, C. Huber, A. Rojano and I. Lopez
In this study, we are interested in regulating two important variables inside of the greenhouse: temperature and CO2 enrichment for two cabins in a experimental greenhouse at the Humboldt University of Berlin. Predicting the behavior of these two variables and photosynthesis will allow us to turn on and off the controls such as heating system, vents opening or CO2 enrichment at the right time, in order to save energy and keep the plants inside of the comfort zone. Artificial Neural Networks (ANN) were used because of their ability to capture the non linear relationships governing the changes in the greenhouse environment. Temperature was predicted 5 and 10 min ahead of the sensor signal, with MSE errors between measured and predicted values of 0.088 and 0.029, respectively. The CO2 predicted from the model was used as an input in the photosynthesis model. In this last model, seven variables were used and the predictions were highly precise with a MSE errors of 0.0563 and 0.0974 for photosynthesis 5 and 10 min ahead, respectively. A sensitivity analysis was performed in the photosynthesis model showing that relative humidity is an important variable for CO2 levels and for the photosynthesis process. The predicting models will allow to achieve our final goal which is to replace the sensors and give predictive information for a higher control quality in an open loop control system.
PDF Fulltext XML References Citation

Related Articles

A Complete CAD Model for Type-I Quantum Cascade Lasers with the use of Artificial Bee Colony Algorithm
Applications of the General Nonlinear Neural Networks in Solving the Inverse Optimal Value Problem with Linear Constraints
Determination the Factors Explaining Variability of Physical Soil Organic Carbon Fractions using Artificial Neural Network
Wireless Sensor Actor Network Based on Fuzzy Inference System for Greenhouse Climate Control
Trends in Automated Systems Development for Greenhouse Horticulture
Role of Greenhouse Technology in Agricultural Engineering
Thermal Modelling for Greenhouse Heating by Using Packed Bed

Leave a Comment

Your email address will not be published. Required fields are marked *