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.

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.

This study evaluated the performance of thermochromic windows as dynamic envelopes for smart greenhouses, focusing on the light environment and cooling load under peak summer conditions. Four covering materials, glass, Low-E glass, polycarbonate, and thermochromic windows, were compared using EnergyPlus (v9.2.0) simulation for an 8-span greenhouse with a floor area of 1008 m² in Gwangju, South Korea, on a representative summer day of 21 July. Thermochromic properties were modeled with temperature-dependent SHGC variations from 0.521 at 25 °C to 0.425 at 85 °C. Results showed that thermochromic windows reduced noon illuminance by 75% compared to conventional glass, from 26,482 lux to 6628 lux, while maintaining adequate light levels above the compensation point for tomato and paprika cultivation. Simultaneously, peak cooling load decreased by 13.1%, from 537,929 W to 467,477 W, outperforming Low-E glass at 9.2% and polycarbonate at 7.0%. At peak hours of 1:00 p.m., when the glass surface temperature reached 60.5 °C, the thermochromic glazing reduced transmitted solar radiation by 37.8% per unit area compared to conventional glass. This study demonstrates that thermochromic windows effectively balance photosynthetic light provision and cooling energy reduction in smart greenhouses, offering a viable design solution for controlled environment agriculture under extreme summer conditions.