The modeling of greenhouse heating and cooling loads at the required operating conditions is important for greenhouse managers or planners. However, the conventional model for the greenhouse thermal load prediction is complex for staff without sufficient academic background. Therefore, a steady-state simplified model based on the estimation of related heat transfer parameters was developed to predict the hourly heating and cooling requirements of the closed greenhouses in Korea and the Northeastern Asian region. In the suggested approach, the thermal load was simplified as a function of greenhouse size, the temperature difference between the setting indoor temperature and the ambient temperature, total horizontal solar radiation, overall heat transfer coefficient, and the fraction factor of solar conversion. Except the designed parameters and the climatic variables, the overall heat transfer coefficient and the fraction factor of the solar conversion were restored using an inverse procedure based on a linear regression approach, which was assessed with synthetic data calculated using the TRNSYS software. The climatic data from meteonorm assisted the simulations for six Northeastern Asian locations. The short-term load profiles and the monthly thermal energy consumptions from eight case studies with different greenhouse sizes and locations were validated with the TRNSYS solutions. The mean bias error and the coefficient of variation of the root-mean-square-error of annual loads were controlled within 5% and 11%, respectively. Satisfactory results suggested that the simplified model could be used for the greenhouse thermal load estimation especially in Korea and the Northeastern Asian region. However, the model should be tested with more regions in future work for extensive applications.

在所需的操作条件下对温室供热和制冷负荷进行建模对于温室管理者或计划者而言非常重要。但是，对于没有足够学术背景的工作人员来说，温室热负荷预测的常规模型很复杂。因此，建立了基于相关传热参数估计的稳态简化模型，以预测韩国和东北亚地区封闭温室的每小时供热和制冷需求。在建议的方法中，将热负荷简化为温室大小，室内温度与环境温度之间的温差，总水平太阳辐射，总传热系数以及太阳能转化率的函数。除设计参数和气候变量外，使用基于线性回归方法的逆过程恢复总的传热系数和太阳能转换的分数因子，并使用TRNSYS软件计算出的合成数据对其进行评估。来自meteonorm的气候数据辅助了东北亚6个地点的模拟。TRNSYS解决方案验证了来自八个不同温室大小和位置的案例研究的短期负荷曲线和每月热能消耗。年负荷的平均偏差误差和均方根误差的变异系数分别控制在5％和11％之内。令人满意的结果表明，该简化模型可用于温室热负荷估算，尤其是在韩国和东北亚地区。但是，在以后的工作中，应该在更多地区测试该模型，以进行广泛的应用。