The continued use of fossil fuels is contributing to severe environmental pollution and the establishment of an abnormal climate; consequently, alternative renewable energy sources are being actively investigated worldwide. Further, following global trends, numerous countermeasures aimed at improving carbon neutrality, promoting sustainable agriculture, and reducing fossil fuel dependence are being implemented in the Republic of Korea. Therefore, this study was conducted to investigate the application of renewable energies for greenhouse heating in the Republic of Korea. Three hybrid systems, numbered 1–3, were constructed using a pellet boiler, hydrothermal heat pump, and solar heat collection system, respectively. Thereafter, the heating performance, combined heat efficiency, energy consumption per temperature lifting, and energy cost per temperature lifting of the systems were compared. The combined thermal efficiency results showed no significant differences. However, in terms of energy consumption and cost, hybrid system 1 demonstrated 25.7 and 24.1% savings, respectively, compared with the other systems. Moreover, based on economic analysis via the net present value and life cycle cost analysis methods, the system reduced costs by 29.2 and 27.7%, respectively, compared with conventional fossil fuel boilers. Thus, hybrid system 1 was identified as the most economical system.

Although smart farms are considered an alternative to traditional agriculture, they require large amounts of energy and high investment costs, hindering their efficient implementation. In the Republic of Korea, the energy supply is primarily for heating rather than cooling, necessitating the accurate prediction of the greenhouse internal temperature to determine the feasibility of agricultural management while using renewable energy. This study developed a model (TRNSYS) for predicting the internal temperature of a greenhouse using building energy simulation. A greenhouse heating experiment was conducted using a hybrid heating system simulated by TRNSYS to analyze the prediction model. The regression analysis of the experimental and simulation results revealed an R2 and RMSE of 0.8834 and 3.61, respectively. A comparative analysis was conducted with the existing hot air heating system to evaluate the heating performance and economic feasibility of the hybrid system. Overall, the heating performance exhibited satisfactory results, whereas the economic analysis, based on life cycle cost, revealed a cost reduction effect of 9.45%. Hence, greenhouse heating using renewable energy can replace conventional fossil fuels with economic advantages. Moreover, the prediction of the internal temperature of the greenhouse will facilitate the design of a systematic smart farm business to prevent duplicate investment.