Integrated Light and CO2 Control to Optimize Commercial Greenhouse
Plant Growth and Energy Efficiency
T.J. Shelford1,2, L.D. Albright1, D.S. de Villiers1,2
1. Cornell UniversityIthaca, NY
2. CEA SystemsIthaca, NY
LASSI: Light and Shade System Implementation
Rule based control of supplemental lighting and shading
Two major goalsControl light integral to a set targetDo so at the lowest cost
Virtual Integral
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CO2 LASSI: TheoryBased on minimizing total cost of
supplemental PARMakes decisions hourlyDetermines the optimal level of CO2 and
lighting to reduce the total cost of supplementation for the rest of the day.
Assumes that the rest of the supplementation for the remainder of the day can only be met through Lighting
Cost of CO2To determine rate of loss of CO2 need to
estimate the required ventilation rate for temperature control, and infiltration losses
Perform a heat balance with estimated values of outdoor temperature and solar input for the coming hour, coupled with the heat load from any supplemental lighting.
Cost of CO2 for the hour is the cost to raise the CO2 from the existing level to the target plus losses
CO2 LASSI Theory:Program tries CO2 concentrations between
ambient and 1600 PPM, with lights On and Off and determines the amount of PAR remaining until target
The cost to provide this remaining PAR is then calculated and added to the cost of CO2 and Light for the current hour
The program then selects and implements the lowest cost option
CO2 LASSI: Implementation
Utilized National Instruments LabVIEW
Tested code first using Java
Utilized the formula node functionality within LabVIEW to implement the more complex text based code.
CO2 LASSI Operation:
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LASSI Operation:
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CO2 LASSI Evaluation:•Two greenhouse compartments in the Kenneth Post Laboratory complex•One section controlled by CO2 LASSI, the other with our basic control program that controls to a set target•30 lettuce plants (cv. Flandria) in each compartment
CO2 LASSI Evaluation:Simulation predicts that the algorithm will
save 40% of our lighting costs:50% savings of electricityOffset by a 10% increase in cost due to CO2
Our measured results to date match the simulation
Will collect performance data through the Spring and Summer.
Next Steps:To modify the program to work with the new
24 hour notice electricity rate structure
To couple this algorithm with a temperature control program to allow temperature setpoints to drift upwards to preserve CO2
Thanks to:USDA for providing the funding to pursue
this project through an SBIR with CEA Systems.