1. T.J. Shelford 1,2, L.D. Albright 1, D.S. de Villiers 1,2 1. Cornell University Ithaca, NY 2. CEA Systems Ithaca, NY

2. LASSI: Light and Shade System Implementation Rule based control of supplemental lighting and shading Two major goals Control light integral to a set target Do so at the lowest cost

3. Virtual Integral

4. CO 2 LASSI: Theory Based on minimizing total cost of supplemental PAR Makes decisions hourly Determines 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

5. Cost of CO 2 To 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

6. CO 2 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

7. CO 2 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.

8. CO 2 LASSI Operation:

9. LASSI Operation:

10. CO 2 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

11. CO 2 LASSI Evaluation: Simulation predicts that the algorithm will save 40% of our lighting costs: 50% savings of electricity Offset 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.

12. 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

13. Thanks to: USDA for providing the funding to pursue this project through an SBIR with CEA Systems.