Energy management of a building-integrated rooftop greenhouse (BiRTG) is considered one of the important factors. Accordingly, the interest in energy simulation models has increased. Energy load computed from the simulation model can be used for appropriate capacity calculation and optimal operation of the environmental control system. In particular, because the thermal environment of greenhouses is sensitive to the external weather environment, dynamic energy simulations, such as building energy simulation (BES), play an essential role in understanding the complex mechanisms of heat transfer in greenhouses. Depending on the type and crop density, there is a significant difference in the thermal energy loads of greenhouses. Furthermore, ventilation is also an important factor affecting the energy input of the greenhouse. Therefore, this study aimed to design and validate BES models considering the crop and ventilation characteristics of a naturally ventilated greenhouse before designing and evaluating a BES model for the BiRTG. First, the BES module for the greenhouse and crop models was designed using field-measured data, and the ventilation characteristics were analysed using computational fluid dynamics (CFD). The greenhouse BES model was designed and then validated by comparing air temperature (T_a) and relative humidity (RH) measured at the greenhouse with the BES-computed results of the greenhouse model. The results showed that the average absolute error of T_a was 1.57 °C and RH was 7.7%. The R² of the designed BES model for T_a and RH were 0.96 and 0.89, respectively. These procedures and sub-modules developed were applied to the energy load calculation of BiRTG.

The duck industry ranks sixth as one of the fastest-growing major industries for livestock production in South Korea. However, there are few studies quantitatively predicting the internal thermal and moisture environment of duck houses. In this study, high-accuracy recurrent neural network (RNN) models were used to predict the internal air temperature and relative humidity of mechanically and naturally ventilated duck houses. The models were developed according to the type of duck houses, seasons, and environmental variables by learning the monitoring data of the internal and external environments. The optimal sequence length of learning data for the development of the RNN model was selected as 120 min. As a result of the validation, both air temperature and relative humidity could be accurately predicted within 1% error. In addition, simplified RNN models were additionally developed by learning only from the data of external air temperature, relative humidity, and duck weight, which are relatively easy to acquire at the farms. The accuracy of the simplified RNN models was similar to the basic model for predicting the internal air temperature and relative humidity of duck houses in real time. In the future, for the convergence of information and communications technologies (ICTs) and application of smart farms in duck houses, the RNN models of duck houses developed in this study can be applied to predict and control the internal environments of duck houses using the model predictive control (MPC) technique.

Building-integrated rooftop greenhouses (BiRTGs) are innovative vertical farms consisting of a greenhouse on the roof of a building. BiRTGs can provide environmental benefits by recycling energy, carbon dioxide, and water between the greenhouse and the building. Moreover, BiRTGs can reduce cooling and heating loads by reducing the exposure of the building surface to heat gains/losses through the roof. However, the benefits of BiRTGs have not yet been completely elucidated from an energy perspective. This study aimed to analyse the energy-saving efficiency of BiRTGs using building energy simulations (BES) and computational fluid dynamics (CFD) techniques. BES is a calculation method for analysing the heating and cooling loads of buildings; however, it was difficult to consider time-dependent changes in the ventilation characteristics in the BES model. CFD can be used to calculate more detailed ventilation characteristics of an experimental facility. Thus, CFD and the BES were combined to obtain more accurate BES-based data. The BES-computed annual energy load for a single-span greenhouse in which tomatoes were grown was 490,128 MJ, whereas the annual energy load for growing tomatoes in a BiRTG resulted in a 5.2% reduction, on average (464,673 MJ). The energy-saving effects were positive from October to April because the BiRTG helped transmit heat energy transmitted from the building to the greenhouse. Regarding the total energy load in the BiRTG after alternating the air temperature management (ATM), the heating energy load was reduced in the winter. ATM was expected to apply from November to March, with average energy savings of 11.8%.

As the pig industry develops rapidly, various problems are appearing both inside and outside the pig houses. In particular, in the case of pig houses, it is difficult to solve the main problems even if automation and mechanization are applied with ICT technology. The main current issues are: (1) preventing infectious diseases amongst livestock, (2) reducing the emission of harmful gas and odors, (3) improving the growth environment inside the pig house, (4) reducing energy costs, (5) improving the farm management and operating system, and 6) improving the livestock product quality. Air recirculation technology can be applied as a technology that can solve these typical problems in the pig industry. An air-recirculated ventilation system can minimize the inflow of air from outdoors and recycle the internal thermal energy of the pig house. The air-recirculated ventilation system consists of (1) an air scrubber module, (2) external air-mixing module, (3) UV cleaning module, (4) solar heat module, and (5) air-distribution module. First, in this study, the field data were collected to analyze the main problems of the target piglet house for the application of the air-recirculated ventilation system. In addition, a computational fluid dynamics (CFD) model was developed and validated for seasonal aerodynamic analysis. The applicability of the air-recirculated ventilation system was evaluated based on the CFD computed results for various environmental conditions. As a result of evaluating the internal environment according to the ventilation rates and external-air-mixing ratio of the air-recirculated ventilation system, the required ventilation rate and external air-mixing ratio to maintain the proper temperature and gas concentration were determined.

A Venlo-type greenhouse with a continuous roof vent (CR-Venlo greenhouse) was proposed by the Ministry of Agriculture, Food and Rural Affairs, South Korea (2019) for natural ventilation even during summers. It is ventilated through the buoyancy effect of the heated air using the high eave elevation. However, the CR-Venlo greenhouse was not distributed domestically, and its ventilation efficiency was not quantitatively evaluated. We aimed to analyze the natural ventilation efficiency of the greenhouse according to the eave height, using computational-fluid-dynamics. The simulation model was analyzed for hot summer conditions. The target greenhouse is ventilated only through the roof vent with all roof windows open; therefore, the air introduced through the roof window is easily exhausted. To evaluate the efficiency of ventilation, the external air entering through the roof window was calculated and evaluated. The amount of incoming air varied greatly with the location of the span and average temperature of the greenhouse; The temperature of the crop zone decreased lognormally with increasing height of the eave. Moreover, the ventilation efficiency of CR-Venlo greenhouse could be increased by improving the ventilation structure such as a shape, position or combination of roof window.