A new greenhouse energy model for predicting the year-round interior microclimate of a commercial greenhouse in Ontario, Canada

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Alex Nauta, Jingjing Han, Syeda Humaira Tasnim, William David Lubitz
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引用次数: 0

Abstract

Modelling the energy use and microclimate of a greenhouse can be a valuable tool for commercial growers, making it possible to predict the impact of making changes to greenhouse systems and operation. This allows energy saving scenarios to be identified and can reduce energy use costs. In this study, a lumped capacitance thermal model is developed to simulate the greenhouse interior microclimate based on exterior conditions and operating settings. The current study incorporated many aspects of a complex commercial greenhouse not commonly seen in literature, such as evaporative cooling pads, dehumidification technology, gas burners, energy curtains, supplementary heating and lighting, and forced ventilation. The model was successfully validated at multiple greenhouse sections of a commercial greenhouse during spring, summer, and fall conditions in the southern Ontario climate. Data was collected from the greenhouse from March to November of 2019 at 15-minute intervals. The measured interior temperature and relative humidity data were used to evaluate the accuracy of the model simulations, while other measurements, such as solar radiation, were used as model inputs. The study greenhouse was unique, as potted rose crops were cycled between sections during the growth stage. This made variation in plant properties relatively small during the different seasons. Detailed information on the model methodology was included to improve reader’s understanding. Overall, the model accuracy is comparable or even better when compared to similar models in the literature, proving it is versatile and can be used as a design tool moving forward. In the future, the current model will be used to conduct comparative analyses of a range of different energy-use reduction technologies and operating procedures (including year-round production) to quantify the most economically and practically feasible options specifically for Ontario greenhouse growers.

Abstract Image

用于预测加拿大安大略省商业温室全年室内小气候的新温室能量模型
对温室的能源使用和小气候进行建模对商业种植者来说可能是一个有价值的工具,使其能够预测改变温室系统和操作的影响。这样就可以确定节能方案,并降低能源使用成本。本文基于室外条件和操作条件,建立了集总电容热模型来模拟温室室内小气候。目前的研究纳入了文献中不常见的复杂商业温室的许多方面,如蒸发冷却垫、除湿技术、燃气燃烧器、能源窗帘、补充供暖和照明以及强制通风。该模型在安大略省南部春季、夏季和秋季气候条件下的商业温室的多个温室部分成功验证。从2019年3月到11月,每隔15分钟从温室收集数据。测量的室内温度和相对湿度数据被用来评估模式模拟的准确性,而其他测量数据,如太阳辐射,被用作模式输入。研究温室是独特的,因为盆栽玫瑰作物在生长阶段在不同的区段之间循环。这使得植物特性在不同季节的变化相对较小。详细介绍了模型的方法,以提高读者的理解。总体而言,与文献中的类似模型相比,该模型的准确性相当甚至更好,证明了它的通用性,可以用作向前发展的设计工具。未来,目前的模型将用于对一系列不同的节能技术和操作程序(包括全年生产)进行比较分析,以量化安大略省温室种植者最经济和最实际可行的选择。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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