Simulation of Greenhouse Management in the Subtropics, Part I: Model Validation and Scenario Study for the Winter Season

Luo, Weihong; Zwart, H.F. de; DaiI, Jianfeng; Wang, Xiaohan; Stanghellini, C.; ChongXing, Bu

doi:10.1016/j.biosystemseng.2004.11.008

Cited by 63 publications

(42 citation statements)

References 12 publications

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“…The air temperature is simulated accurately, with overall values of ME of 0.3°C and RMSE of 1.6°C, which represents values accepted as good by several authors (Wang and Boulard, 2000;Cunha, 2003;Luo et al, 2005;Coelho et al, 2006). Air relative humidity, accepted as the most difficult parameter to estimate due to the dependence of the air temperature, showed ME of -0.8% and RMSE of 7%, these results are in accordance with others published by Zhang et al (1997), Navas et al (1998) and Salgado and Cunha (2005).…”

Section: Validationsupporting

confidence: 71%

Greenhouses climate modelling. Tests, adaptation and validation of a dynamic climate model

Baptista¹,

Bailey²,

Meneses³

et al. 1970

Span. j. agric. res.

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Most greenhouse climate models are specific for a particular combination of greenhouse type, crop, region and weather conditions. Models are formulated and validated for those conditions and it is not easy to directly extrapolate them to other, different conditions. In order to use them the coefficients need to be calibrated by experimental work, followed by validation of the adapted model. The main purpose of this work was the application of a formal dynamic climate model, defined and validated for heated greenhouses in continental regions of Spain, to non heated greenhouses in a mild winter region at the coast of Portugal. The original model was tested, adapted and validated so it simulated the microclimate inside unheated greenhouses. The methodology used enabled the problems to be identified, the model to be modified in a systematic way and then re-run to determine the improved performance. The new model includes new properties for some boundary components and sub-models for ventilation and stomatal resistance applicable to this greenhouse-crop system and new expressions for the convection heat transfer coefficients. In the validation process predicted and measured variables were compared graphically to show trends in the data and by using statistical parameters to characterise model performance. The model was validated with data representing different weather, ventilation operation and tomato crop conditions. Good agreement between predicted and measured data was obtained. It has been proved that this model can be used to estimate the greenhouse climate conditions, based on the weather conditions and on the greenhouse-crop system characteristics.Additional key words: convection heat transfer coefficients, nocturnal ventilation, unheated greenhouses. Resumen Modelado del clima de los invernaderos. Experimentación, adaptación y validación de un modelo climático dinámicoLa mayor parte de los modelos climáticos de invernaderos son formulados y verificados para aquellas condiciones concretas en que fueron definidos, como son el tipo de invernadero, el cultivo y las características climáticas. Para aplicarlos en condiciones distintas, los coeficientes de los modelos deben ser calibrados, proceso seguido de la correspondiente validación del modelo adaptado. El objetivo principal del presente trabajo fue la aplicación de un modelo climático dinámico formal, definido y validado para invernaderos calefactados del interior de España con clima continental, a invernaderos no calefactados de la costa de Portugal con clima templado. La metodología empleada en este estudio permitió identificar los problemas del proceso de adaptación, modificando el modelo de una forma sistemática, para después determinar su comportamiento mejorado. El nuevo modelo climático incluye nuevas propiedades para algunos componentes de contorno, nuevos submodelos para la ventilación y la resistencia estomática del sistema invernadero-cultivo, y nuevas expresiones para los coeficientes de transferencia convectiva de calor. Para el proceso d...

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Section: Validationsupporting

confidence: 71%

Greenhouses climate modelling. Tests, adaptation and validation of a dynamic climate model

Baptista¹,

Bailey²,

Meneses³

et al. 1970

Span. j. agric. res.

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“…The quantification of the energy requirements of glasshouse food production has been investigated previously in the literature, for example in studies such as those in ASABE (2008), Chalabi et al (2002), Gupta and Chandra (2002), Moreton and Rowley (2012), Ozkan et al (2011aOzkan et al ( , 2011b, Papadopoulos and Hao (1997), Subi c et al (2015), Mariani et al (2016), Luo et al (2005) and Wass and Barrie (1984). In Papadopoulos and Hao (1997), the authors investigated the effect of the glasshouse cover material in three tomato glasshouses focusing on yields, energy requirements and productivity, whereas Gupta and Chandra (2002) focused on the effects of a glasshouse's design on its energy requirements, with a particular interest in the shape and orientation of a glasshouse in India.…”

Section: Introductionmentioning

confidence: 99%

“…Focusing on the prediction of internal environment parameters of glasshouses Vanthoor et al (2011) presented a methodology for describing the effects of weather and glasshouse design parameters, thus predicting aspects such as temperature, CO 2 concentration and vapour pressure. Similarly, Luo et al (2005) focused on the Chinese subtropical climate conditions for predicting the glasshouse's microclimate conditions for summer and winter periods. In addition, Luo et al (2005) identified the impact of set temperature on energy consumption and crop mass production.…”

Section: Introductionmentioning

confidence: 99%

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A generic tool for quantifying the energy requirements of glasshouse food production

Georgiou

Acha

Shah

et al. 2018

Journal of Cleaner Production

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a b s t r a c tQuantifying the use of resources in food production and its environmental impact is key to identifying distinctive measures which can be used to develop pathways towards low-carbon food systems. In this paper, a first-principle modelling approach is developed, referred to as gThermaR (Glasshouse-Thermal Requirements). gThermaR is a generic tool that focuses on the energy requirements of protected heated production, by integrating holistic energy, carbon, and cost modelling, food production, data analytics and visualization. The gThermaR tool employs historic data from weather stations, growing schedules and requirements specific to grower and product needs (e.g. set-point temperatures, heating periods, etc.) in order to quantify the heating and cooling requirements of glasshouse food production. In the present paper, a case study is reported that employs a database compiled for the UK. Another relevant feature of the tool is that it can quantify the effects that spatial and annual weather trends can have on these heating and cooling requirements. The main contribution of this work, therefore, concerns the development a tool that can provide a simple integrated approach for performing a wide range of analyses relevant to the thermal requirements of heated glasshouses. The tool is validated through collaborations with industrial partners and showcased in a case study of a heated glasshouse in the UK, offering the capacity to benchmark and compare different glasshouse types and food growth processes. Results from the case study indicate that a significant reduction in the heating requirement and, therefore, carbon footprint, of the facility can be achieved by improving key design and operational parameters. Results indicate savings in the peak daily and annual heating requirements of 44e50% and 51e57% respectively, depending on the region where the glasshouse is located. This improvement is also reflected in the carbon emissions and operating costs for the different energy sources considered. Furthermore, the temporal variability/uncertainty of the annual energy requirements and of the peak daily energy requirements are found to be considerably lowered through improvements to the glasshouse attributes. Overall, gThermaR proves its value in quantifying and identifying key factors that have a significant impact on energy requirements of heated glasshouses. Such valuable outputs are invaluable for stakeholders in the food industry that have an interest in mapping the sustainability and mitigating the carbon footprint of their supply chain processes.

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“…Humidity affects growth of greenhouse crops mainly through its impact on leaf size and light interception rather than through a direct impact on photosynthesis by increasing the stomata conductance at low VPD (Vapor this sense, programmable Logic Devices (PLDs) present as a good option for the technology development and implementation, because PLDs allow fast development of prototypes and the design of complex hardware systems using FPGAs (Field Programmable Gate Arrays) and Complex Programmable Logic Devices [9][10][11]. Real-time monitoring provides reliable, timely information of crop and soil status, important in taking decisions for crop production improvement [12][13][14][15]. Evaluation of agricultural production systems is a time consuming and difficult process because it means performing visits to selected crop fields to be able to measure and register certain physical, chemical and biological characteristics of the cultivated areas [16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

Field Programmable Gate Array System for IoT Applications

Elmedany¹

2016

IJCNT

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Internet of Things (IoT) becomes an attractive field of research in the last decade. The main objective of IoT is enabling the integration and interconnection of the physical world and the cyber space. The aim of such space is telecommunications of the online world of computer networks and the Internet. The recent advances in sensors network and embedded systems have helped in the development and realization of Internet of Things. In order to provide adapted services to the users, such spaces should operate in a proactive manner and according to the current context. A Field Programmable Gate Array (FPGA) is a programmable chip designed to be configured after manufacturing by a designer or a customer using special computer interface hardware tools. The FPGA configuration is generally specified using a Hardware Description Language (HDL), there are many applications nowadays that are using FPGA-based design as the main controller instead of using ASICs (Application Specific Integrated circuits) design. This paper presents the hardware implementation of an FPGA (Field Programmable Gate Array) based design for IoT (Internet of Things) applications. The main contribution in this paper is to introduce a cost effective design to be used for the applications of IoT, the FPGA based design has been described using VHDL (VHSIC Hardware Description Language), simulated using Xilinx ISE 14.7 software tools, and then implemented on Xilinx Virtex-7 FPGA VC707 Evaluation platform, targeting xc7vx485t-2ffg1761 device.

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Simulation of Greenhouse Management in the Subtropics, Part I: Model Validation and Scenario Study for the Winter Season

Cited by 63 publications

References 12 publications

Greenhouses climate modelling. Tests, adaptation and validation of a dynamic climate model

Greenhouses climate modelling. Tests, adaptation and validation of a dynamic climate model

A generic tool for quantifying the energy requirements of glasshouse food production

Field Programmable Gate Array System for IoT Applications

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