1. The Intelligent Greenhouse
Budapest University of Technology and Economics
Department of Measurement and Information Systems
Peter Eredics
The Intelligent Greenhouse

2. Greenhouses
Greenhouses are built in various sizes and for many different purposes.

3. Actuators Common actuators in greenhouses: Windows Roof vents
Shading curtains
Heating
Irrigation
Misting
etc.

4. Traditional Control
Traditional solution: independent, set-point based controllers.
Advantages:
Simple and cheap solutions, available off the shelf
Low complexity hardware with high reliability
Unambiguous operation

5. Traditional Control
Traditional solution: independent, set-point based controllers.
Drawbacks:
The set-points have to be specified by the operator
The control is strongly reactive
Missing synchronization of the actuators

6. Computer Control Solutions
Computers are only used for:
data recording,
visualization,
remote access.

7. Optimal Control
The goal is to maintain the physical parameters inside the greenhouse in the optimum range required by the plants with the lowest possible cost.
Requirements:
Maximize the comfort of the plants
Minimize costs

8. The Intelligent Greenhouse
Overtaking the drawbacks of traditional control solutions, with AI methods, to realize optimal control.
Set-points Goals
Reactive control Predictive control
Missing synchronization Planning

9. Goals of the Control
Traditional control left all responsibility to the operator.
The operator knows the plants, not the thermal behavior of the greenhouse.
The intelligent control should accept and work directly based on the goals of the operator.

10. Goals of the Control

11. Predictive Control
Traditional control can only react when undesirable situations are reported by its sensors.
Traditional control does not utilize regular external influences (i.e. sunset)

13. Planning and Actuator Synchronization
The intelligent control creates and evaluates plans shared for all actuators.
A reasonable forward planning time is 4 hours.
The space of potential plans is very large: introduction of constraints or incremental planning is required.

14. Planning - Example

15. Modeling the Greenhouse
The model has to predict the future thermal state of the greenhouse
The system has large complexity
The greenhouse is in always in change, thus adaptive modeling is essential

16. The Experimental Greenhouse

17. Thermal Zones Roof vents Shading screen Covered desk Heating pipe 4 3
Side window 2 1
/ / Temperature / Light / Humidity measurement point

18. Topology
Using 7 microcontrollers for measuring 44 physical quantities on 23 locations every 5 minutes

19. The Experimental Control System
The control system records measurements, runs a traditional control application and provides remote control interface for data extraction and intelligent control testing.

20. Data visualization Temperature data Light levels Actuator states
Cloud coverage

21. Data Cleaning

22. Data Cleaning – Missing Desk Temperatures

23. The Proposed Model Decomposition

24. 1. Local External Temperature Forecast24

25. 2. Missing Regional Weather Data Restoration
L = lokálisan mért hőmérséklet – ismert
R = lokálisan mért megvilágítás - ismert, az utolsó néhány mérés átlaga
G = regionális hőmérséklet adat – ezt szeretnénk pótolni az előbbiek ismeretében 25

26. 3. Heating Pipe Model Heating System Model Predicted Temperature
Current Temperatures
Warming Pipe Model
Cooling Pipe Model
Heating Control Signal Sequence 26

27. 4. Global Greenhouse Model (1)

28. 4. Global Greenhouse Model (2)

29. 4. Global Greenhouse Model (3)

30. 4. Global Greenhouse Model (4)

31. Results The measurement system is collecting data.
The data cleaning system is operating.
Submodels have been implemented.
The global greenhouse model is under development…

32. Thank you for your attention!