Modeling of Energy Demand of a High-Tech Greenhouse in Warm Climate Based on Bayesian Networks

This work analyzes energy demand in a High-Tech greenhouse and its characterization, with the objective of building and evaluating classification models based on Bayesian networks. The utility of these models resides in their capacity of perceiving relations among variables in the greenhouse by identifying probabilistic dependences between them and their ability to make predictions without the need of observing all the variables present in the model. In this way they provide a useful tool for an energetic control system design. In this paper the acquisition data system used in order to collect the dataset studied is described. The energy demand distribution is analyzed and different discretization techniques are applied to reduce its dimensionality, paying particular attention to their impact on the classification model’s performance. A comparison between the different classification models applied is performed.