Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach

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dc.contributor.authorMisbaudeen Aderemi Adesanya
dc.contributor.authorHammed Obasekore
dc.contributor.authorAnis Rabiu
dc.contributor.authorWook-Ho Na
dc.contributor.authorQazeem Opeyemi Ogunlowo
dc.contributor.authorTimothy Denen Akpenpuun
dc.contributor.authorMin-Hwi Kim
dc.contributor.authorHyeon-Tae Kim
dc.contributor.authorBo-Yeong Kang
dc.contributor.authorHyun-Woo Lee
dc.date.accessioned2024-05-09T07:32:00Z
dc.date.available2024-05-09T07:32:00Z
dc.date.issued2024
dc.description.abstractThe control of indoor temperature in greenhouses is crucial as it directly impacts the crop’s thermal comfort and the performance of heating, ventilation, and air-conditioning (HVAC) systems. Conventional feedback controllers, like on/off, can sometimes make HVAC system work at full capacity when only half that capacity is needed. In contrast, the proportional-integral-derivative (PID) controller, provides precise control based on its P, I, and D parameters. However, it lacks a formal design procedure for optimizing a specified objective function. Previous studies have utilized conventional PID tuning approaches to track room setpoint temperature for residential buildings, data centers, and office buildings, with limited research in greenhouse applications. To address this gap, this study proposes a flexible PID controller that employs a deep reinforcement learning (DRL) algorithm to optimize its parameters, by tracking the setpoints and energy consumption of a greenhouse planted with tomatoes. This approach is different from the typical method of using the trained RL agent directly in HVAC controls. Through a self-made TRNSYS-Python cosimulation framework, the DRL agent interacts directly and in real time with the greenhouse and its plants. Consequently, optimized PID parameters were established and tested in the simulated environment. The resulting performance, in terms of both energy consumption and its ability to maintain the crop’s comfort temperature, was compared with the simulated on/off and manually tuned PID controllers. Compared to the on/off baseline control, the proposed PID optimized parameters reduce energy use by 8.81% to 12.99%, and the manually tuned PID parameters with the Ziegler-Nichols tuning method reduce energy use by 7.17 %. Additionally, the proposed method had a deviation of 2.07% to 3.13%, while the manually tuned PID controller and the on/off controller had deviations of 7.27% and 3.27%, respectively, from the minimum comfortable temperature. This study serves as a framework for improving the energy efficiency of greenhouse HVAC system operations.
dc.identifier.otherhttps://doi.org/10.1016/j.eswa.2024.124126
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417424009928?dgcid=coauthor
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/13927
dc.language.isoen
dc.publisherElsevier
dc.subjectTRNSYS
dc.subjectPython
dc.subjectOptimization
dc.subjectDeep reinforcement learning
dc.subjectCosimulation
dc.titleDeep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach
dc.typeArticle

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