A stochastic bottom-up model for space heating and domestic hot water load profiles for German households
A stochastic bottom-up model for space heating and domestic hot water load profiles for German households
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DOI：
10.1016/j.enbuild.2016.04.069
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发表时间：
2016-07-15
影响因子：
6.7
通讯作者：
Wilde-Haussmann, Bernhard
中科院分区：
文献类型：
--
作者：
Fischer, David;Wolf, Tobias;Wilde-Haussmann, Bernhard
2013年，德国住宅部门83%的能源用于制备生活热水（13%）和空间供暖（70%）。热需求曲线对于正确确定加热技术的操作和尺寸至关重要。在这项工作中，将电力负荷的随机自底向上方法扩展到涵盖生活热水（DHW）和空间供暖需求。该方法适用于单个建筑和住宅区，并与目前使用的方法进行了验证和比较。使用行为模型来确定DHW水龙头，电器使用和建筑物的温度设置。建筑热负荷的计算使用简化的物理模型，以允许现实的能源需求概况，有效的模型参数化和快速计算。基于集群建筑类型的建筑热负荷随机化方法，提出了建筑参数和供暖设置的变化，允许模拟大量类似建筑。对德国单户住宅测量数据的验证表明，典型的日负荷分布中，DHW消耗的相关性为0.92，平均相对误差为3%，空间供暖的相关性为0.89，平均相对误差为9%。(C) 2016 Elsevier B.V.版权所有
In 2013 83% of energy in-the German residential sector is used for the preparation of domestic hot water (13%) and space heating (70%). Thermal demand profiles are essential to correctly determine operation and sizing of heating technologies. In this work, the stochastic bottom-up approach for electric loads is extended to cover domestic hot water (DHW) and space heating demands. The approach is presented for individual buildings and residential areas, validated and compared to currently used approaches. A behavioural model is used to determine DHW tappings, electric appliance use and temperature settings of the building. Building heat load is calculated using a simplified physical model, to allow for realistic energy demand profiles, efficient model parametrisation and fast computation. A randomisation approach for building heat load based on a clustered building typology, a variation of building parameters and heating settings is presented which allows the simulation of larger quantities of similar buildings. Validation against measured data for German single family houses shows a correlation of the typical daily load profile for DHW consumption of 0.92 and a mean relative error of 3% and for space heating 0.89 and 9% respectively. (C) 2016 Elsevier B.V. All rights reserved.