Energy-Saving Control Algorithm of Venlo Greenhouse Skylight and Wet Curtain Fan Based on Reinforcement Learning with Soft Action Mask

Lihan Chen, Lihong Xu () and Ruihua Wei
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Lihan Chen: College of Electronics and Information Engineering, Tongji University, Cao’an Road, No. 4800, Shanghai 201804, China
Lihong Xu: College of Electronics and Information Engineering, Tongji University, Cao’an Road, No. 4800, Shanghai 201804, China
Ruihua Wei: College of Electronics and Information Engineering, Tongji University, Cao’an Road, No. 4800, Shanghai 201804, China

Agriculture, 2023, vol. 13, issue 1, 1-16

Abstract: Due to the complex coupling of greenhouse environments, a number of challenges have been encountered in the research of automatic control in Venlo greenhouses. Most algorithms are only concerned with accuracy, yet energy-saving control is of great importance for improving economic benefits. Reinforcement learning, as an unsupervised machine learning method with a framework similar to that of feedback control, is a powerful tool for autonomous decision making in complex environments. However, the loss of benefits and increased time cost in the exploration process make it difficult to apply it to practical scenarios. This work proposes an energy-saving control algorithm for Venlo greenhouse skylights and wet curtain fan based on Reinforcement Learning with Soft Action Mask (SAM), which establishes a trainable SAM network with artificial rules to achieve sub-optimal policy initiation, safe exploration, and efficient optimization. Experiments in a simulated Venlo greenhouse model show that the approach, which is a feasible solution encoding human knowledge to improve the reinforcement learning process, can start with a safe, sub-optimal level and effectively and efficiently achieve reductions in the energy consumption, providing a suitable environment for crops and preventing frequent operation of the facility during the control process.

Keywords: greenhouse control; energy saving; reinforcement learning; learning with knowledge (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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