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An Improved Intelligent Control System for Temperature and Humidity in a Pig House

Author

Listed:
  • Hua Jin

    (School of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Gang Meng

    (School of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Yuanzhi Pan

    (Artificial Intelligence Lab, Zhenjiang Hongxiang Automation Technology Co., Ltd., Zhenjiang 212000, China
    School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
    Faculty of Business and Economics, The University of Hong Kong, Hong Kong 999077, China)

  • Xing Zhang

    (School of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Changda Wang

    (School of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

Abstract

The temperature and humidity control of a pig house is a complex multivariable control problem. How to keep the temperature and humidity in a pig house within a normal range is the problem to be solved in this paper. The traditional threshold-based environmental control system cannot meet this requirement. In this paper, an intelligent control system of temperature and humidity in a pig house based on machine learning and a fuzzy control algorithm is proposed. We use sensors to collect the temperature and humidity in the pig house and store these data in chronological order. Then, we use these time series data to train the GRU model and then use the GRU model to predict the temperature and humidity change curve in the pig house in the next 24 hours. Finally, the mathematical model of the pig house and related equipment is established, and the output power of the related equipment is calculated based on the prediction results of GRU so as to effectively regulate the indoor temperature and humidity. The experimental results show that compared with the threshold-based environmental control system, our system reduces the abnormal temperature and humidity by about 90%.

Suggested Citation

  • Hua Jin & Gang Meng & Yuanzhi Pan & Xing Zhang & Changda Wang, 2022. "An Improved Intelligent Control System for Temperature and Humidity in a Pig House," Agriculture, MDPI, vol. 12(12), pages 1-21, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:1987-:d:981900
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    References listed on IDEAS

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    1. Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).
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    Cited by:

    1. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    2. Haopu Li & Haoming Li & Bugao Li & Jiayuan Shao & Yanbo Song & Zhenyu Liu, 2023. "Smart Temperature and Humidity Control in Pig House by Improved Three-Way K-Means," Agriculture, MDPI, vol. 13(10), pages 1-22, October.

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