Saridakis, G.1 ; Dalamagidis, K.; Kolokotsa, D.; Stavrakakis, G.S.; Koutroulis, E.; Kalaitzakis, K.; Antonidakis, E.; Chatzakis, J.; Kaliakatsos, I.
May 2006
WSEAS Transactions on Environment and Development , vol 2 issue 5 p 690-7
The microclimate control in a greenhouse is a difficult and complicated procedure since the factors that modulate the clime are several and dependant of each other. This work is an effort of controlling the most of these factors with a conjunction of the most possible low energy consumption. A microclimate system control is designed based on artificial intelligence techniques. Two fuzzy logic controllers are developed embodies the expert knowledge of the agriculturists and the growers. These controllers consist of fuzzy P (Proportional) and PD (Proportional-Derivative) control using desired clime set points. The factors that being monitored are the greenhouse's indoor luminance value, temperature, relative humidity, CO2 concentration and the outside temperature, actuating in automations as heating units, motor-controlled windows, motor controlled shading curtains, artificial lighting, CO2 enrichment bottles and water fogging valves. These controllers obtain the best possible microclimate for any cultivation setting the desired parameters into the set-points. They prototyped in Matlab environment and tested through a greenhouse model, which was designed into TRNSYS IISIBAT software. For the model an algorithm developed predicting ambient greenhouse air conditions to be used for energy efficiency simulation and control schemes optimization. The climatic conditions considered are temperature, relative humidity, CO2 concentration and solar radiation. The algorithm has two modes of operation, the first simulates the greenhouse while in the second the heating, cooling, humidification or dehumidification, CO2 injection rates are calculated to maintain certain set points. The algorithm is designed to be used with the TRNSYS 15 simulation software which provides the pre-processing of the weather data, as well as controller models. The model is defined by several components that describe the characteristics of each glazing surface, the plants, the floor, the equipment and the zone itself. Using this approach it is possible to simulate any greenhouse structure, provided that the required information is availableSource: www.engineeringvillage2.org