Skip to main content

Advertisement

Log in

An optimization scheme for IoT based smart greenhouse climate control with efficient energy consumption

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) has attracted tremendous research attention in the recent past fromindustry and academia. IoT is quite helpful in uplifting living standards by transforming conventional technology into smart systems. Greenhouse production is considered as an ultimate solution for rising global food demands with the growing population. Greenhouse provides a year-round production facility for fresh vegetables with around 50% increased production rate in comparison to open-air cultivation. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. In this paper, we have proposed a novel optimization scheme that aims to achieve a trade-off between energy consumption and desired climate setting in greenhouse i.e. temperature, \({\mathrm{CO}}_2\) level, and humidity. For performance evaluation of the proposed system, we have developed an ad-hoc emulator of the greenhouse environment. For the proposed model validation and experimental analysis, we have used 15 days of external environmental data collected in Jeju, South Korea. Proposed optimization scheme results are compared with a baseline scheme. Comparative analysis of experimental results shows that our proposed model maintains desired indoor environment for maximizing crop production with 26.56% reduced energy consumption than the baseline scheme. Furthermore proposed model achieve a 27.76% cost reduction when compared to the baseline scheme. Better optimization results of the proposed scheme give us the confidence to further investigate its effectiveness in a real environment for achieving improved energy efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Asare-Bediako B, Ribeiro PF, Kling WL (2012) Integrated energy optimization with smart home energy management systems. In: 2012 3rd IEEE PES innovative smart grid technologies Europe (ISGT Europe). IEEE, pp 1–8

  2. Bozchalui MC, Cañizares CA, Bhattacharya K (2014) Optimal energy management of greenhouses in smart grids. IEEE Trans Smart Grid 6(2):827–835

    Article  Google Scholar 

  3. Chen L, Du S, He Y, Liang M, Xu D (2018) Robust model predictive control for greenhouse temperature based on particle swarm optimization. Inf Process Agric 5(3):329–338

    Google Scholar 

  4. Cplex I (2007) 11.0 user’s manual. ILOG SA, Gentilly, France p 32

  5. De Angelis F, Boaro M, Fuselli D, Squartini S, Piazza F (2013) A comparison between different optimization techniques for energy scheduling in smart home environment. In: Neural nets and surroundings. Springer, pp 311–320

  6. Deng S, Xiang Z, Taheri J, Mohammad KA, Yin J, Zomaya A, Dustdar S (2020) Optimal application deployment in resource constrained distributed edges. IEEE Trans Mobile Comput 20(5):1907–1923. https://doi.org/10.1109/TMC.2020.2970698

  7. Deng S, Zhang C, Li C, Yin J, Dustdar S, Zomaya AY (2021) Burst load evacuation based on dispatching and scheduling in distributed edge networks. IEEE Trans Parallel Distrib Syst 32(8):1918–1932

    Article  Google Scholar 

  8. Fourer R, Gay DM, Kernighan BW (2002) Ampl: a modeling language for mathematical programming. brooks. Duxbury Press/Brooks/Cole Publishing Company, New York

    Google Scholar 

  9. Hasni A, Taibi R, Draoui B, Boulard T (2011) Optimization of greenhouse climate model parameters using particle swarm optimization and genetic algorithms. Energy Procedia 6:371–380

    Article  Google Scholar 

  10. Lorestani A, Ardehali M, Gharehpetian GB (2016) Optimal resource planning of smart home energy system under dynamic pricing based on invasive weed optimization algorithm. In: 2016 Smart grids conference (SGC). IEEE, pp 1–8

  11. Marsh J (2013) Automated plant watering system. US Patent 8,584,397

  12. Meteoblue (2018) Weather Jeju City. https://www.meteoblue.com/en/weather/forecast/week/jeju-city_republic-of-korea_1846266. Accessed 01-Oct-2018

  13. Mirzamohammadi S, Jabarzadeh A, Shahrabi MS (2020) Long-term planning of supplying energy for greenhouses using renewable resources under uncertainty. J Clean Prod 264:121611

    Article  Google Scholar 

  14. Ouammi A, Achour Y, Zejli D, Dagdougui H (2019) Supervisory model predictive control for optimal energy management of networked smart greenhouses integrated microgrid. IEEE Trans Autom Sci Eng 17(1):117–128

    Article  Google Scholar 

  15. Ouammi A, Choukai O, Zejli D, Sayadi S (2020) A decision support tool for the optimal monitoring of the microclimate environments of connected smart greenhouses. IEEE Access 8:212094–212105

    Article  Google Scholar 

  16. Pedrasa MAA, Spooner TD, MacGill IF (2010) Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans Smart Grid 1(2):134–143

    Article  Google Scholar 

  17. Shamshiri RR, Bojic I, van Henten E, Balasundram SK, Dworak V, Sultan M, Weltzien C (2020) Model-based evaluation of greenhouse microclimate using iot-sensor data fusion for energy efficient crop production. J Clean Prod 263:121303

    Article  Google Scholar 

  18. Shen Y, Wei R, Xu L (2018) Energy consumption prediction of a greenhouse and optimization of daily average temperature. Energies 11(1):65

    Article  Google Scholar 

  19. Tsui KM, Chan SC (2012) Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans Smart Grid 3(4):1812–1821

    Article  Google Scholar 

  20. Ullah I, Kim D (2017) An improved optimization function for maximizing user comfort with minimum energy consumption in smart homes. Energies 10:11

    Google Scholar 

  21. Ullah I, Kim D (2018) An optimization scheme for water pump control in smart fish farm with efficient energy consumption. Processes 6(6):65

    Article  Google Scholar 

  22. Viani F, Giarola E, Robol F, Oliveri G, Massa A (2014) Distributed monitoring for energy consumption optimization in smart buildings. In: 2014 IEEE conference on antenna measurements and applications (CAMA). IEEE, pp 1–3

  23. Wang J, Li S, Guo S, Ma C, Wang J, Jin S (2014) Simulation and optimization of solar greenhouses in northern jiangsu province of china. Energy Build 78(78):143–152

    Google Scholar 

  24. Zhang Y, Zeng P, Zang C (2015) Optimization algorithm for home energy management system based on artificial bee colony in smart grid. In: 2015 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER). IEEE, pp 734–740

  25. Zhao H, Deng S, Liu Z, Yin J, Dustdar S (2019) Distributed redundant placement for microservice-based applications at the edge. arXiv:1911.03600

  26. Zhuang P, Liang H, Pomphrey M (2018) Stochastic multi-timescale energy management of greenhouses with renewable energy sources. IEEE Trans Sustain Energy 10(2):905–917

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (2019M3F2A1073387), and this research was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (No. 2018-0-01456, AutoMaTa: Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT). Any correspondence related to this paper should be addressed to Dohyeun Kim.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Israr Ullah.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ullah, I., Fayaz, M., Aman, M. et al. An optimization scheme for IoT based smart greenhouse climate control with efficient energy consumption. Computing 104, 433–457 (2022). https://doi.org/10.1007/s00607-021-00963-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-021-00963-5

Keywords

Mathematics Subject Classification

Navigation