基于随机空调用能行为的住区能耗模拟方法及案例研究

Methodology and Case Study of Energy Consumption Simulation in Settlements Based on Stochastic Air Conditioning Energy Use Behavior

针对当前城镇住宅能耗预测研究中基于固定空调用能行为作息,导致能耗模拟结果与实际情况差异很大,且多数研究聚焦在单体建筑预测的研究现状,本文建立了一套空调及开窗随机用能行为模拟方法,模拟获得全年空调及开窗的随机序列,并发现模拟结果与问卷统计结果误差不超过5%,验证了该方法的合理性。在此基础上,通过聚类分析选取杭州市住区典型建筑,依据典型建筑的面积及单体建筑空调能耗强度,得到住区空调能耗。研究结果发现,采用固定空调用能行为计算得到的住区制冷空调年耗电量(26.5 kW·h/m^(2))比采用随机空调用能行为(22.9 kW·h/m^(2))高15.72%,采暖年耗电量(26.1 kW·h/m^(2))比采用随机空调用能行为(24.6 kW·h/m^(2))高6.1%。本研究为住区能耗的准确模拟提供方法支撑,对后续基于建筑运行能耗的电网削峰填谷以及节能策略的制定有重要意义。

The current urban residential energy consumption prediction research is primary based on fixed air conditioning energy use behaviors,resulting in a big difference between energy consumption simulation results and the actual result,and most of the research focus on the single building prediction.To this end,this paper established a set of air conditioning and window opening stochastic energy behavior simulation methods,conducted simulation to obtain the full-year air conditioning and windows random sequences,and found that the error between the simulation results and the questionnaire statistical results is no greater than 5%,thus verifying the rationality of the method.On this basis,typical buildings in the settlement of Hangzhou were selected through cluster analysis,and the air conditioning energy consumption in the settlement was obtained based on the area of the typical buildings and the air conditioning energy intensity of individual buildings.It was found that the annual power consumption of refrigeration and air-conditioning(26.5 kW·h/m^(2))calculated by the fixed energy use behavior was 15.72%higher than that using the random energy use behavior(22.9 kW·h/m^(2)),and the annual power consumption of heating(26.1 kW·h/m^(2))was 6.1%higher than that using the random energy use behavior(24.6 kW·h/m^(2)).This study provides methodological support for the accurate simulation of energy consumption in settlements,bearing significance for the subsequent development of grid peak shaving and valley filling as well as energy saving strategies based on the energy consumption of building operation.