Smart Farm System Study
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스마트팜 시스템 연구
Smart Farm System Study
◇ Research Background
As smart farm technologies continue to advance, improving energy efficiency has become an essential area of research in modern agriculture. Smart farms utilize Information and Communication Technology (ICT) to maximize agricultural productivity, and maintaining optimal environmental conditions is particularly important for greenhouse cultivation. Due to climate change, energy consumption for greenhouse heating and cooling has increased, placing financial burdens on farm operations. Therefore, it is necessary to analyze the energy performance of smart farms and derive optimal operating strategies.
Previous studies have primarily focused on comparing the performance of heat pump systems with boiler systems or estimating the energy required to maintain specific temperatures within greenhouses. However, comprehensive studies that consider the effects of greenhouse envelope structure, HVAC system types, and regional climate conditions on energy consumption remain limited. Thus, this study aims to evaluate the energy performance of smart farms according to crop characteristics, envelope configurations, and HVAC system types, and to propose optimal operating strategies.
◇ Research Methods and System Description
This study conducted TRNSYS 18 simulations to evaluate the energy performance of a smart farm greenhouse located in Eumseong, Chungcheongbuk-do. The greenhouse has a total area of 330 m² and is used primarily for melon cultivation. Multiple simulation variables were established based on the greenhouse’s envelope structure and HVAC system.
The first variable was regional climate conditions. Outdoor temperature data for Eumseong, Changwon, and Cheorwon were applied to compare energy consumption across regions.
The second variable involved greenhouse envelope properties, analyzing indoor temperature changes based on the thickness of the air insulation layer (5 mm and 10 mm) in double glazing and the presence or absence of shade screens.
The third variable examined heating and cooling systems, comparing the heating performance and energy cost of electric boilers and diesel boilers to determine the optimal HVAC method.
The smart farm’s HVAC system consists of a carbon-rod electric boiler, fan coil units (FCUs), a thermal energy storage (TES) tank, and a circulation pump. Seasonal heating and cooling loads and energy consumption were computed through simulation.
Simulation results showed that the smart farm’s energy consumption varied significantly depending on regional climate conditions, envelope structure, and HVAC system type.
Regional climate comparison:
Changwon recorded 11% lower annual energy consumption than Eumseong, while Cheorwon showed 3% higher consumption. This is because Changwon’s mild climate reduces heating/cooling loads, while Cheorwon’s cold winters increase heating demand.
Effect of shade screens:
Installing shade screens reduced indoor temperature by 10% and lowered total summer cooling energy consumption by 1,955,855 kJ.
Effect of insulation thickness:
Increasing the air insulation layer in double glazing from 5 mm to 10 mm reduced annual total energy consumption by 17%, leading to a 14.7% reduction in heating electricity cost.
Heating system comparison:
Electric boilers reduced annual energy consumption by 14% compared to diesel boilers, and heating costs decreased by 12.7%.
This study comprehensively analyzed various factors influencing the energy performance of smart farms and proposed strategies to help greenhouse operators reduce energy costs and improve operational efficiency. The results indicate that selecting climate-appropriate HVAC systems, enhancing envelope insulation, and using shade screens are effective strategies for improving smart farm energy performance.
Future research should include experimental validation across different smart farm models to develop more accurate energy prediction tools. Additional studies are also needed on smart farm operation strategies incorporating renewable energy sources. The findings of this study are expected to contribute to sustainable smart farm operations and the realization of environmentally friendly agriculture.
