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Energy efficient operation and modeling for greenhouses: A literature review

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  • Iddio, E.
  • Wang, L.
  • Thomas, Y.
  • McMorrow, G.
  • Denzer, A.

Abstract

With growing food demand worldwide, controlled environment agriculture is an important strategy for crop production year-round. One of the important types of controlled environment agriculture is greenhouses. Key indoor environmental parameters such as carbon dioxide, moisture, lighting, and temperature are required to be maintained for favorable crop growth in greenhouses. Due to lightweight construction and inefficient operation, greenhouses consume more fossil fuel energy in the operation of mechanical systems than other similar sized buildings and have larger carbon footprints. In fact, greenhouses are one of the most energy-intensive sectors of the agricultural industry. Energy consumption in greenhouses is influenced by mechanical systems, indoor environment, crop growth, and evapotranspiration. Therefore, energy simulations help analyze the complex thermal processes in greenhouse operation, and contribute to energy efficient greenhouse operation. This paper reviews existing strategies on energy efficient control operation and state-of-the-art energy simulation for greenhouses. It first discusses strategies for improving energy efficiency in greenhouse control operation by summarizing the studies on energy efficient operation strategies, the control of key greenhouse parameters, sensing network and monitoring systems, along with various control algorithms. Second, the review covers energy modeling of greenhouses by summarizing existing and developed approaches. Finally, this review identifies areas in which future research has the potential to reduce greenhouse energy consumption and carbon footprint.

Suggested Citation

  • Iddio, E. & Wang, L. & Thomas, Y. & McMorrow, G. & Denzer, A., 2020. "Energy efficient operation and modeling for greenhouses: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    • Handle: RePEc:eee:rensus:v:117:y:2020:i:c:s1364032119306884
    DOI: 10.1016/j.rser.2019.109480
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