Greenhouse climate hierarchical fuzzy modelling

Salgado, Paulo; Boaventura‐Cunha, José

doi:10.1016/j.conengprac.2004.05.007

Cited by 78 publications

(54 citation statements)

References 15 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: 79%

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: 79%

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 AUTOPIA program at the IAI, of which this work forms a part, has much experience in the use of fuzzy logic. This technique has been used for the control of systems as diverse as helicopters [21] and the air temperature and humidity of greenhouses [22]. For this reason, it was regarded as a good potential solution to the problem of controlling unmanned vehicles.…”

Section: Fuzzy Controllermentioning

confidence: 99%

Controller for Urban Intersections Based on Wireless Communications and Fuzzy Logic

Milanés

Pérez

Onieva

et al. 2010

IEEE Trans. Intell. Transport. Syst.

150

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“…It is a generalization of the Probabilistic Clustering Algorithm (FCM), here applied to rules instead of points. With this algorithm, the system obtained from the data is transformed into a new system, organized into several subsystems, in PCS structures (Salgado, 2005b and2007a). The paper is organized as follows: firstly, a brief introduction to fuzzy systems is presented.…”

Section: Introdutionmentioning

confidence: 99%

Probabilistic Clustering Algorithms for Fuzzy Rules Decomposition

Salgado

Igrejas

2007

IFAC Proceedings Volumes

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The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system results a set of decomposed sub-systems that will be conveniently linked into a Parallel Collaborative Structures.

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Greenhouse climate hierarchical fuzzy modelling

Cited by 78 publications

References 15 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

Controller for Urban Intersections Based on Wireless Communications and Fuzzy Logic

Probabilistic Clustering Algorithms for Fuzzy Rules Decomposition

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