The random variation of meteorological parameters affects both building load and energy system design. Although long-term historical meteorological data can reflect this characteristic, a large amount of high-dimensional meteorological data can bring great difficulties to the design process. Therefore, a simplified method of generating meteorological parameters for an uncertainty-based energy system design is proposed in this study. To simplify the meteorological elements, a sensitivity analysis is first carried out based on the maximum information coefficient. Then, combined with the deterministic model and stochastic model of the meteorological parameters, a mixed time-series meteorological model is proposed to reflect the uncertainty. Finally, to generate random meteorological data, based on information entropy theory, Monte Carlo simulation method is adopted and improved by determining the minimum number of simulations. The efficiency and accuracy of this method are verified using a solar heating system design as an example. Results show that the characteristics of uncertainty and time sequence in the meteorological data generated by this method can be well retained. Moreover, the calculation amount can be reduced by 89.3% compared with the original design method.

气象参数的随机变化会影响建筑负荷和能源系统设计。尽管长期的历史气象数据可以反映出这一特征，但是大量的高维气象数据可能给设计过程带来很大的困难。因此，本研究提出了一种用于基于不确定性的能源系统设计的简化的生成气象参数的方法。为了简化气象要素，首先基于最大信息系数进行敏感性分析。然后，结合气象参数的确定性模型和随机模型，提出了一种反映时间不确定性的混合时间序列气象模型。最后，要基于信息熵理论生成随机气象数据，通过确定最小仿真次数，采用并改进了蒙特卡洛仿真方法。以太阳能加热系统设计为例，验证了该方法的效率和准确性。结果表明，该方法生成的气象数据具有不确定性和时间序列的特征。而且，与原始设计方法相比，计算量可以减少89.3％。