Using Artificial Neural Networks to Predict the Climate in a Greenhouse: First Simulation Results on a Semi-Closed System
This study evaluated if an Artificial Intelligence climate forecasting model can be considered as a useful tool for saving energy in semi-closed greenhouses. Preliminary results are presented on the 5-Minutes prediction of the internal air temperature and humidity modeled with Artificial Neural Networks (ANN). Since the final goal of the simulation is to integrate the predictions in a control system, the inputs were selected according to the standard signals in control theory: Set Points, Perturbations and Current State Vector. These inputs were: energy for heating, energy taken from cooling, ventilation opening, thermal screen opening, outside conditions (temperature, relative humidity, solar radiation, wind velocity) and current internal conditions (temperature and relative humidity). Data for the models were recorded in 2011, taken of 30-seconds-intervals. The ANN was created, trained and validated using different data sets. The prediction showed a very good fit to measured data and suggests that the ANN methods can be used to make short-term climate predictions, which are useful to take control actions before the trigger setpoints are reached.
Miranda, L.; Schuch, I.; Dannehl, I.; Rocksch, T.; Salazar, R.; Schmidt, U. 2015. Using Artificial Neural Networks to Predict the Climate in a Greenhouse: First Simulation Results on a Semi-Closed System. Acta Horticulturae 1099, 137-144.