4.5 Article

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

期刊

COMPUTING
卷 104, 期 2, 页码 433-457

出版社

SPRINGER WIEN
DOI: 10.1007/s00607-021-00963-5

关键词

Energy efficiency; IoT; Optimization; Smart greenhouse

资金

  1. Energy Cloud R&D Program through the National Research Foundation of Korea(NRF) - Ministry of Science, ICT [2019M3F2A1073387]
  2. Institute for Information & Communications Technology Planning & Evaluation (IITP) - Korea Government (MSIT) [2018-0-01456]

向作者/读者索取更多资源

The application of IoT in greenhouse production can significantly increase crop yield and reduce energy consumption and costs. By optimizing and controlling the greenhouse climate, more efficient production can be achieved. Further research will explore more possibilities for improving energy efficiency.
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, CO2 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, SouthKorea. 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.

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