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greenhouse climate models

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 EFITA 2003 Co nferen ce 5-9. July 200 3, Debrec en, Hung ary 823 GREENHOUSE CLIMATE MODELS: AN OVERVIEW José Boaventu r a Cu nh a, [email protected] UTAD – Universidade de Trás-os-Montes e Alto Douro, Dep. Engenharias CETAV – Centro de Estudos e de Tecnologias do Ambiente e Vida 5001-911 Vila Real, Portugal Abstract: Greenhouse climate and crop models are essential for improving environmental management and control efficiencies. In this paper, are described several types of models that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamical behaviours of the inside air temperature, humidity and carbon dioxide concentration models and their domains of application. Linear and non- linear mode ls will be covered, focu sing on issues such as: physical models, black-box mod els, and neural networks models. Several experiments will be presented to illustrate the performance of each model in the simulation and prediction of the greenhouse climate. The models are described as functions of the outside climate, the control actions performed and the transpiration and  phot osynt hesis res ponse s of the plan ts. The dat a used to co mput e the simu latio n model s were acqu ired in an experimental greenhouse using a sampling time interval of 1 minute. The greenhouse is automated with several actuators and sensors that are connected to an acquisition and control system  base d on a perso nal co mput er. Keywords: Crop models, Greenhouse climate models, Neural networks, System identificatio n 1. Introduction To improve the management and control of a greenhouse climate, an adaptive climate control strategy must be used to compute the optimal control signals used for a defined cost  performance function. The adaptive  PID – Proportional Integral Derivative controller structure showed in  figure 1 could be used for this purpose. The adaptive controller needs the use of greenhouse climate models to predict future outputs based on past and current inputs, the expected control actions and the predicted weather inputs. An optimiser module computes the proportional, integral and derivative gains, in order to minimize or maximize the specified cost function according to the constraints imposed. Afterwards, these parameters are sent to the  PID closed controlled loop.

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