Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse

Author:

Bersani ChiaraORCID,Fossa MarcoORCID,Priarone Antonella,Sacile RobertoORCID,Zero EnricoORCID

Abstract

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

Reference75 articles.

1. Eurostathttps://ec.europa.eu/eurostat/statistics-explained/index.php?title=Farms_and_farmland_in_the_European_Union_-_statistics

2. Eurostathttps://ec.europa.eu/eurostat/statistics-explained/index.php?title=Performance_of_the_agricultural_sector

3. From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges

4. Enhancing the ability of agriculture to cope with major crises or disasters: What the experience of COVID-19 teaches us

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Camera-based plant growth monitoring for automated plant cultivation with controlled environment agriculture;Smart Agricultural Technology;2024-08

2. Precision Agriculture and UAV's;Advances in Computational Intelligence and Robotics;2024-05-31

3. Control of Production-Inventory Systems of Perennial Crop Seeds;2024

4. A Qualitative Review of Smart Farming in ASEAN;2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM);2023-12-18

5. Model-based quantitative analysis in two-time-scale decomposed on–off optimal control of greenhouse cultivation;Information Processing in Agriculture;2023-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3