Short-term external air temperature prediction for an intelligent greenhouse by mining climatic time series

Climate control for intelligent greenhouses is currently an active field of research. Model based intelligent greenhouse control systems seem to increase the control performance over traditional solutions. In this paper the control intelligence means the preferably minimal maintenance (heating) cost, within the climatic conditions required to grow sensitive floral cultures. To this purpose it is not enough merely to follow optimally some particular temperature profile. Costs can be minimized if the internal air temperature is predictable within a reasonable time span, and this depends on how well we can predict the dynamics of the external air temperature. The problem is closely related to the prediction of the actual weather conditions within the immediate environment of the greenhouse. The paper demonstrates the limited performance of uninformed, simple methods for temperature forecasts, and introduces more accurate solutions using information from the problem domain.