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.