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Title: Greenhouse heat load prediction using a support vector regression model
Authors: Coelho, J.P.
Cunha, José Boaventura
Oliveira, Paulo
Pires, Eduardo S.
Issue Date: 2010
Citation: Coelho, João; Cunha, José; Oliveira, Paulo; Pires, Eduardo S. (2010) - Greenhouse heat load prediction using a support vector regression model. Soft Computing Models in Industrial and Environmental Applications.
Abstract: Modern greenhouse climate controllers are based on models in order to simulate and predict the greenhouse environment behaviour. These models must be able to describe indoor climate process dynamics, wchich are a function of the both the control actions taken and the outside climate. Moreover, if predictive or feedforward control techniques are to be applied, it is necessary to employ models to describe and predict the weather. From all the climate variables, solar radiation is the one with greater impact in the greenhouse heat load. Hence, making good predictions of this physical quantity is of extreme importance. In this paper, the solar radiation is represented as a time-series and a support vector regression model is used to make long term predictions. Results are compared with the ones achieved by using other type of models, both linear and non-linear.
Peer review: yes
Appears in Collections:DE - Publicações em Proceedings Indexadas ao ISI/Scopus

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