فرزاد فقيهي حبيب ابادي

In recent years, the increasing global deman‎d for sustainable agricultural production has led to a rapid expansion in the use of greenhouses as controlled environments for cultivation. Accurate prediction an‎d control of greenhouse microclimate variables play a crucial role in optimizing energy consumption an‎d improving plant productivity. In this study, a dynamic an‎d integrated greenhouse model based on an existing library was developed using SimulationX software an‎d the Modelica language, configured for the target study conditions. The developed model includes the main greenhouse components—cover layer, vegetation, natural ventilation, fogging system, heating unit, an‎d shading curtain—an‎d operates based on mass an‎d energy balance between the subsystems. Model validation against reference temperature data demonstrated that indoor temperature prediction could be achieved with a mean absolute error of approximately 1.1 °C, a root mean square error of about 1.3 °C, an‎d a coefficient of determination of 0.96. Subsequently, the greenhouse performance was eva‎luated for four representative periods (May, June, July, an‎d January) in two cases, with an‎d without shading curtains, for the climate conditions of Pakdasht, Tehran. Psychrometric analysis using charts an‎d tables indicated that increasing solar radiation an‎d air temperature during warm months cause the internal climate conditions to drift outside the comfort range, while in transitional months such as May an‎d June, relatively high outdoor humidity reduces the effectiveness of evaporative cooling. Furthermore, to provide a quantitative assessment, simulation data were classified using the developed artificial intelligence model (k-Nearest Neighbors), an‎d the percentage of time spent within the thermal comfort zone was extracted. With the application of shading curtains, the percentage of time with a comfort score greater than 0.9 was 94.5% in May, 58.2% in June, an‎d 56.7% in July. The results indicate that the developed physical model can serve as a tool for feasibility assessment of yet–to-be-constructed greenhouses, improvement of climatic performance in existing ones, an‎d generation of data for developing artificial-intelligence-based microclimate control systems in greenhouses.

شبيه‌سازي گلخانه , مدل‌سازي فيزيكي , سيموليشن‌ايكس , ريزاقليم , پرده سايه‌انداز , امكان‌سنجي , هوش مصنوعي

Greenhouse simulation , Physical modeling , SimulationX , Microclimate , Shading curtain , Feasibility study , Artificial intelligence

Farzad Faghihi HabibAbadi

Dr. Norouz Mohammad Nouri