基于人行为模型的住宅小区冷负荷模拟方法研究

Study on Occupant Behavior Based Modeling Method to Simulate Cooling Loads in Residential Districts

近年来,我国的区域供冷系统在城市住宅小区中得以广泛应用。大多数系统存在选型偏大的现象,初投资浪费、系统运行效率低,造成能源的浪费。因此,准确预测区域建筑的冷负荷是应用区域供冷系统亟待解决关键问题之一。现在常用域供冷设计中常采用负荷模拟算法均对人行为做了简化处理,不能全面反映不同用户由于人行为造成的负荷差异性,可能造成系统负荷的不合理估计。为了探究人行为对于住宅小区尖峰负荷、总负荷等方面的影响程度,本文基于实测和问卷调研数据对武汉市某住宅小区建立DeST模型并展开模拟,对比了采用详细的人行为模型与全时间全空间模型、固定作息模型的模拟结果,及实测结果的冷负荷差异,结果表明过于简化的人行为模型会造成系统尖峰负荷和总负荷的过大估计,考虑人行为差异的模型更贴近实际测试结果。本研究也对此方法在住宅小区冷负荷模拟应用展开了探讨。

District cooling systems are widely used in urban residential communities in China. Most of such systems are oversized, which leads to wasted investment, low operational efficiency and, thus, a waste of energy. The accurate prediction of district cooling loads that can support the rightsizing of cooling plant equipment remains a challenge. This study develops a novel stochastic modeling method that consists of six prototype house models representing most apartments in a district, occupant behavior models of residential buildings reflecting their spatial and temporal diversity as well as their complexity based on a large-scale residential survey in China, and a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was applied to a case study using the Designer＇s Simulation Toolkit（DeST） to simulate the cooling loads of a residential district in Wuhan, China, results of which show that the simulation results agreed well with the measured data.