Abstract
Greenhouse agriculture is a crucial solution to global food security and sustainability challenges, as it provides a controlled environment for plant growth, resulting in higher yields and efficient resource utilization. Climate control plays a critical role in determining energy consumption and plant growth within greenhouse systems. The selection and optimization of control parameters have a significant impact on the overall performance. This study conducted simulations of a tomato greenhouse located in Montreal, Canada, with the aim of evaluating the effect of different control setpoints in the presence of high-pressure sodium (HPS) supplemental lighting and light-emitting diode (LED) supplemental lighting on greenhouse performance. To comprehensively assess the influence of each control setpoint, a sensitivity analysis (SA) was performed, systematically varying the control setpoints over a wider range than what is typically observed in tomato production. The SA utilized different control setpoints as inputs, while energy consumption and crop yield were considered as outputs. The setpoints for relative humidity and air temperature during the light period were identified as the most influential factors. This highlights the importance of accurate measurements and predictions of temperature and humidity to optimize environmental conditions in indoor greenhouses when implementing a predictive control strategy. The results obtained from this SA can contribute to the development of reduced-order models that focus on the most influential variables.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- \(C{O}_{2}^{ppm}\):
-
Carbon dioxide (CO2) concentration, ppm
- \({I}_{Glob}\):
-
Global solar radiation, W/m2
- \({p}_{Band}\):
-
Tolerance band for control, -
- \(RH\):
-
Relative humidity, %
- \(Sum{I}_{Glob}\):
-
Daily global solar radiation sum, MJ/m2/day
- \(T\):
-
Temperature, ◦C
- \(Air\):
-
Inside air
- \(blScr\):
-
Black screen parameters
- \(Dark\):
-
Period when the lamps are off
- \(Day\):
-
Period from sunrise to sunset
- \(Lamps\):
-
Lamps parameters
- \(Light\):
-
Period when the lamps are on
- \(Night\):
-
Period from sunset to sunrise
- \(SP\):
-
Setpoint
- \(thScr\):
-
Thermal screen parameters
References
United Nation, World Population Prospects 2019: Highlights, Department of Economic and Social Affairs (2019). https://population.un.org/wpp/Publications/Files/WPP2019_10KeyFindings.pdf
Food and Agriculture Organization of the United Nations, Ed., The future of food and agriculture: trends and challenges. Rome: Food and Agriculture Organization of the United Nations, 2017
Rizwan, A., Khan, A.N., Ahmad, R., Kim, D.H.: Optimal environment control mechanism based on OCF connectivity for efficient energy consumption in greenhouse. IEEE Internet Things J. 10(6), 5035–5049 (2023). https://doi.org/10.1109/JIOT.2022.3222086
Duarte-Galvan, C., et al.: Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems. Span J. Agric. Res. 10(4), 926 (2012)https://doi.org/10.5424/sjar/2012104-487-11
Zhang, S., Guo, Y., Zhao, H., Wang, Y., Chow, D., Fang, Y.: Methodologies of control strategies for improving energy efficiency in agricultural greenhouses. J. Clean. Prod. 274, 122695 (2020). https://doi.org/10.1016/j.jclepro.2020.122695
Chimankare, R.V., Das, S., Kaur, K., Magare, D.: A review study on the design and control of optimised greenhouse environments. J. Trop. Ecol. 39, e26 (2023). https://doi.org/10.1017/S0266467423000160
Soussi, M., Chaibi, M.T., Buchholz, M., Saghrouni, Z.: Comprehensive review on climate control and cooling systems in greenhouses under hot and arid conditions. Agronomy 12(3), 626 (2022). https://doi.org/10.3390/agronomy12030626
Katzin, D., van Mourik, S., Kempkes, F., van Henten, E.J.: GreenLight – an open source model for greenhouses with supplemental lighting: evaluation of heat requirements under LED and HPS lamps. Biosys. Eng. 194, 61–81 (2020). https://doi.org/10.1016/j.biosystemseng.2020.03.010
Katzin, D., Marcelis, L.F.M., van Mourik, S.: Energy savings in greenhouses by transition from high-pressure sodium to LED lighting. Appl. Energy 281, 116019 (2021). https://doi.org/10.1016/j.apenergy.2020.116019
Priva | Smart horticulture & building management solutions, Priva. https://www.priva.com/. Accessed 25 May 2023
Ridder | Innovative technical solutions for protected horticulture | Inside greenhouse technology for profitable horticulture, Ridder. https://ridder.com/ (Accessed 25 May 2023)
Complete Greenhouse Projects - Dalsem, Dalsem. https://www.dalsem.com/en (Accessed 25 May 2023)
Certhon Growing anything, anywhere. For everyone., Certhon. https://certhon.com/ Accessed 25 May 2023)
Havecon | Horticultural Projects, Havecon. https://havecon.com/en/. Accessed 25 May 2023
Payne, H.J., Hemming, S., Van Rens, B.A.P., Van Henten, E.J., Van Mourik, S.: Quantifying the role of weather forecast error on the uncertainty of greenhouse energy prediction and power market trading. Biosys. Eng. 224, 1–15 (2022). https://doi.org/10.1016/j.biosystemseng.2022.09.009
Stanghellini, C. van’t Ooster, B. Heuvelink, E.: Greenhouse horticulture, Technology for optimal crop production. The Netherlands: Wageningen Academic Publishers (2019)
Vanthoor, B.H.E., Stanghellini, C., Van Henten, E.J., De Visser, P.H.B.: A methodology for model-based greenhouse design: Part 1, a greenhouse climate model for a broad range of designs and climates. Biosys. Eng. 110(4), 363–377 (2011). https://doi.org/10.1016/j.biosystemseng.2011.06.001
EnergyPlus. https://energyplus.net/weather/sources#CWEC. Accessed 08 Jun 2023
Katzin, D.: Energy saving by LED lighting in greenhouses : a process-based modelling approach. Wageningen University (2021). https://doi.org/10.18174/544434
Turcotte, G. :Production de la tomate de serre au Québec, Syndicat des producteurs en serre du Québec, p. 297, Apr. 2015
Palmitessa, O.D., Pantaleo, M.A., Santamaria, P.: Applications and development of LEDs as supplementary lighting for tomato at different latitudes. Agronomy 11(5), 835 (2021). https://doi.org/10.3390/agronomy11050835
Saltelli, A. (ed.): Sensitivity Analysis in Practice: a Guide to Assessing Scientific Models. Wiley, Hoboken, NJ (2004)
Saltelli, A., Ed., Global Sensitivity Analysis: The Primer. Chichester, England ; Hoboken, NJ: John Wiley (2008)
Dorais, M.: PLG-3207-H23: Cultures en serre (17896, 17897). Université Laval
Kim, R., Kim, J., Lee, I., Yeo, U., Lee, S., Decano-Valentin, C.: Development of three-dimensional visualisation technology of the aerodynamic environment in a greenhouse using CFD and VR technology, part 1: development of VR a database using CFD. Biosys. Eng. 207, 33–58 (2021). https://doi.org/10.1016/j.biosystemseng.2021.02.017
EnergyPlus. https://energyplus.net/weather. Accessed 25 May 2023
Latin Hypercube Sampling vs. Monte Carlo Sampling – Data Science Genie. https://datasciencegenie.com/latin-hypercube-sampling-vs-monte-carlo-sampling/. Accessed 08 Jun 2023
Petelet, M., Iooss, B., Asserin, O., Loredo, A.: Latin hypercube sampling with inequality constraints. AStA Adv. Stat. Anal. 94(4), 325–339 (2010). https://doi.org/10.1007/s10182-010-0144-z
Nguyen, A.-T., Reiter, S.: A performance comparison of sensitivity analysis methods for building energy models. Build. Simul. 8(6), 651–664 (2015). https://doi.org/10.1007/s12273-015-0245-4
Gagnon, R., Gosselin, L., Decker, S.: Sensitivity analysis of energy performance and thermal comfort throughout building design process. Energy and Buildings 164, 278–294 (2018). https://doi.org/10.1016/j.enbuild.2017.12.066
Tian, W.: A review of sensitivity analysis methods in building energy analysis. Renew. Sustain. Energy Rev. 20, 411–419 (2013). https://doi.org/10.1016/j.rser.2012.12.014
Multiple linear regression - MATLAB regress, Matlab. https://www.mathworks.com/help/stats/regress.html. Accessed 25 May 2023
Grégoire, F., Gosselin, L., Alamdari, H.: Sensitivity of carbon anode baking model outputs to kinetic parameters describing pitch pyrolysis. Ind. Eng. Chem. Res. 52(12), 4465–4474 (2013). https://doi.org/10.1021/ie3030467
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Trépanier, MP., Gosselin, L. (2024). Impact of Setpoint Control on Indoor Greenhouse Climate Modeling. In: Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14467. Springer, Cham. https://doi.org/10.1007/978-3-031-48649-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-48649-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48648-7
Online ISBN: 978-3-031-48649-4
eBook Packages: Computer ScienceComputer Science (R0)