In this study, seven of the most commonly applied covering materials in smart farms are selected as the representative samples for analysis, namely, glass, soft film (polyethylene, PE), soft film (polyolefin, PO), rigid plastic film (ethylene tetra fluoro ethylene, ETFE), rigid plastic sheet (poly methyl methacrylate, PMMA), rigid plastic sheet (polycarbonate, PC double layer), and woven film. For each covering material, visible light transmittance and reflectivity, solar radiation transmittance and reflectivity, thickness, solar heat gain coefficient, and the coefficient of heat transmission are measured according to the test methods in the Korean Industrial Standards (KS) to derive input data for the respective materials. In addition, using the optical and thermal input data as derived above, simulations are performed on the cooling load and daylight characteristics of smart farms to derive basic reference data for the selection of adequate covering materials for the design. Based on the analysis result of the daylight characteristics for each covering material, for a shading rate of 60%, the average values of indoor illuminance were 19,879 lux, 20,012 lux, 19,393 lux, 19,555 lux, 16,560 lux, 16,228 lux, and 11,173 lux for glass, PE film, PO film, ETFE, woven film, PMMA, and PC double layer, respectively, between 6 a.m. and 8 p.m., which correspond to the hours when daylight enters indoor spaces. Considering the target light intensity for strawberry growth at 10,000–40,000 lux, the above results confirm that all the sample covering materials had an indoor illuminance level above the lower limit range of the target light intensity. For the cooling load evaluation, the PC double layer had the lowest value of 482.8 W/m², and PO film had the highest value of 633.8 W/m². The difference between the cooling loads of the PC double layer and the PO film is 151 W/m², which amounts to 23.8%, thus indicating that the selected covering material significantly impacts the cooling load. The cooling load exhibited a pattern similar to that of the coefficient of heat transmission and solar heat gain coefficient, which are key influencing factors for most of the sample materials. However, for PMMA, the cooling load was low because it had a higher coefficient of heat transmission than other materials, but its solar heat gain coefficient was relatively low. Another possible reason for the difference is that the solar heat gain coefficient impacts the cooling load. When the cooling set temperature was controlled from Case 1-1 to Case 1-2, the cooling load decreased by 10.7% on average. In addition, when the cooling set temperature changed from Case 1-1 to Case 1-3, the cooling load decreased by 26.1% on average. For cooling set temperature control, maintaining the temperature around the lower temperature range of the optimal growth temperature of the plants increases the yield, but it also incurs increased cooling load and cost. In terms of cost only, while maintaining the cooling temperature for 24 h at 30 °C, which is the upper limit of the optimal growth temperature, would be advantageous, it will lead to a deterioration of the quality and reductions in yield. Therefore, as follow-up studies for further investigation of the findings of this research, there is the need for an evaluation of the yield and quality with respect to the range of cooling set temperatures. When the internal shading rate was increased to 40% (Case 2-2) with reference to Case 2-1, which was a greenhouse without the application of internal shading, the cooling load decreased by 27.4% on average. Furthermore, when the internal shading rate increased to 50% (Case 2-3) with reference to Case 2-1, the cooling load decreased by 29.3% on average. When the internal shading rate increased to 60% (Case 2-4), the cooling load decreased by 31.5% on average.

This study analyzes the performance of thermal energy storage tanks and chillers in efficiently operating cooling systems for smart greenhouses in hot, arid climates such as the United Arab Emirates (UAE). The performance of chillers is heavily influenced by outdoor air temperatures, with the coefficient of performance (COP) of chillers decreasing and energy consumption increasing as daytime temperatures rise. This study found that when the outdoor air temperature reached 46.6 °C, the COP of the chiller dropped to 2.18, representing a 24.6% decrease compared to the COP of 2.89 at 35 °C. Conversely, at night, when the outdoor air temperature dropped to 28.3 °C, the chiller’s performance recovered, with the COP rising to 3.67. To address this, a strategy utilizing thermal energy storage tanks to store chilled water at night for use during the day was proposed, compensating for the decline in chiller performance. The results showed that when the thermal energy storage capacity was set at 40%, energy consumption decreased by up to 15%. However, while increasing the thermal energy storage capacity beyond 50% effectively reduced the peak load on the chiller, further increasing it beyond 60% led to a rise in chiller capacity and energy consumption. Therefore, this study concludes that setting the thermal energy storage capacity to no more than 50% is the most effective strategy to maximize chiller performance and reduce energy consumption. These findings provide crucial guidelines for the design and operation of cooling systems, offering valuable strategies to optimize the operation of smart greenhouses in hot climates.

Approximately 20% of all school facilities, i.e., 7,980 buildings, are at least 40 years old in Korea. As the number of aged buildings is expected to rapidly increase, it is necessary to improve such facilities to protect safe learning environments and save energy. This study aims to develop the energy based benchmark model (EBBM) and utilization for energy saving in school. EBBM can be operated for school buildings, a category encompassing complex buildings with different systems and large gaps between them. To utilization of EBBM allows us to obtain knowledge from the school facility, in order to define and tune data driven analysis rules. Data driven energy analysis also allows ascertaining their expected energy consumption and estimating the possible saving systems by using the building energy flow chart. In this research, Korea education data-base was analyzed to classify energy based benchmark model. Applicability review is conducted through detailed system analysis of 10 schools. The difference between the benchmark model calculated value and the actual value was confirmed to be within 10%. Through the benchmarking model, it is possible to compare energy consumption for buildings of different sizes. Energy consumption by energy source and room type was analyzed through an in-depth status survey. Most of School Classrooms consume the largest amount of energy, with cooling and heating energy constituting the largest proportion.