规划阶段建筑冷热负荷预测与特性分析

Prediction and characteristics analysis of building heating and cooling loads at planning phase

为解决区域建筑能源规划阶段因单体建筑参数不确定带来的冷热负荷预测问题,提出基于拉丁超立方抽样的蒙特卡罗模拟方法,其过程为:首先,确定不确定参数及分布;然后,通过R语言编程对不确定参数抽样并自动生成模型;最后,导入EnergyPlus软件进行负荷计算。以天津某规划用地为例,采用所提出的方法对2000组抽样的建筑冷热负荷进行预测,并对预测结果进行不确定和敏感性分析。研究结果表明:基于拉丁超立方抽样的蒙特卡罗模拟方法实现了抽样的快速收敛和模型的快速生成,可以有效计算区域建筑峰值冷热负荷的频数分布、累积概率和特征值;模型中,太阳得热系数、外窗传热系数、建筑层数、建筑底面长宽比、南向窗墙比和北向窗墙比对冷热负荷影响显著。

In order to solve the problem of heating and cooling loads prediction caused by the uncertainty of single building parameters in regional building energy planning, a Monte Carlo simulation method based on Latin hypercube sampling was proposed. Firstly, the uncertain parameters and their distribution were determined, then the uncertain parameters were sampled by Latin hypercube sampling and models were generated automatically by R programming language. Finally, these models were imported into the EnergyPlus software to calculate the cooling and heating load. Meanwhile, taking a planning land in Tianjin as an example, 2 000 groups of samples were taken and predicted by the proposed method, and the uncertainty and sensitivity of the results were analyzed.The results show that the Monte Carlo simulation method based on Latin hypercube sampling realizes the fast convergence of sampling and the fast generation of model. The frequency distribution,cumulative probability and eigenvalue of peak cooling and heating load of regional buildings can be effectively calculated by this method. Six parameters of the model, including solar heat gain coefficient, external window heat transfer coefficient, number of floors, building aspect ratio, south window to wall ratio, and north window to wall ratio, have significant influence on the building cooling and heating loads.