Alex Nauta, Jingjing Han, Syeda Humaira Tasnim, William David Lubitz
{"title":"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","authors":"Alex Nauta, Jingjing Han, Syeda Humaira Tasnim, William David Lubitz","doi":"10.1016/j.inpa.2023.06.002","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 438-456"},"PeriodicalIF":7.7000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new greenhouse energy model for predicting the year-round interior microclimate of a commercial greenhouse in Ontario, Canada\",\"authors\":\"Alex Nauta, Jingjing Han, Syeda Humaira Tasnim, William David Lubitz\",\"doi\":\"10.1016/j.inpa.2023.06.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":53443,\"journal\":{\"name\":\"Information Processing in Agriculture\",\"volume\":\"11 4\",\"pages\":\"Pages 438-456\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing in Agriculture\",\"FirstCategoryId\":\"1091\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214317323000550\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317323000550","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
A new greenhouse energy model for predicting the year-round interior microclimate of a commercial greenhouse in Ontario, Canada
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.
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