A Model-Based Decision Support System For Temperature Set Point Optimisation For Greenhouse Heating

The optimisation of temperature set point for greenhouse heating can minimize the energy consumption required for greenhouse heating for crop production. To optimise the temperature set point for greenhouse heating, it is necessary to know the interaction between crop gowth processes and greenhouse microclimate. Crop growth and greenhouse microclimate simulation models can quantitatively discribe the interation between crop growth process and greenhouse microclimate, hence are useful tools for the optimisation of temperature set point for greenhouse heating. The objective of this study is to develop a model-based decision support system (DSS) for temperature set point optimisation for greenhouse heating. For this purpose, experiments were carried out in greenhouses in Shanghai during 2001-2005 to collect greenhouse microclimate and crop data for model development and validation. Firstly, a greenhouse microclimate simulation model was developed based on the principle of greenhouse energy and mass (moisture, CO2) balances. Secondly, a microclimate model based energy consumption system to predict the energy consumption for greenhouse heating in winter was developed. Thirdly, a photo-thermal based crop development and growth model system was developed for prediction of crop developmental stage, biomass production and yield. Finally, the greenhouse crop development and growth model and the energy consumption prediction model were combined to develop a DSS for temperature set point optimisation for greenhouse heating.In the microclimate model, the effect of canopy transpiration on greenhouse microclimate was taken into consideration. The input of the model includes outside solar radiation, air temperature, air humidity and wind speed. The output of the model mainly includes air temperature inside greenhouse, relative humidity inside greenhouse and canopy transpiration rate. Experiments were carried out in a Dutch Venlo-type greenhouse in Shanghai during three seasons to collect microclimate and crop data to validate the model. The simulated results agreed well with the measured data. The coefficient of determination R~2 and root mean squared error RMSE between the simulated and the measured air...