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%.