Greenhouse indoor temperature prediction based on extreme learning machines for resource-constrained control devices implementation

  • Paniagua-Tineo A
  • Salcedo-Sanz S
  • Ortiz-García E
 et al. 
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In this paper we present an Extreme Learning Machine approach for a real problem of indoor temperature prediction in greenhouses. In this specific problem, the computational cost of the forecasting algorithm is capital, since it should be implemented in resource-constrained devices, typically an embedded controller. We show that the ELM algorithm is extremely fast, and obtains a reasonable performance in this problem, so it is a very good option for a real implementation of the temperature forecasting system in greenhouses.

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  • A. Paniagua-Tineo

  • S. Salcedo-Sanz

  • E. G. Ortiz-García

  • A. Portilla-Figueras

  • B. Saavedra-Moreno

  • G. López-Díaz

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