Operational Characterisation of Neighbourhood Heat Energy After Large-Scale Building Retrofit

被引:0
|
作者
Beagon, Paul [1 ,2 ]
Boland, Fiona [3 ]
O'Donnell, James [1 ,2 ]
机构
[1] Univ Coll Dublin, Sch Mech & Mat Engn, Belfield 4, Dublin, Ireland
[2] Univ Coll Dublin, Energy Inst, Belfield 4, Dublin, Ireland
[3] Royal Coll Surgeons Ireland, Lower Mercer St 2, Dublin, Ireland
基金
爱尔兰科学基金会;
关键词
Building retrofit; Building simulation; Modelica; AixLib library Neighbourhood scale; Statistical distribution; SYSTEMS;
D O I
10.1007/978-3-030-00662-4_19
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
To achieve housing retrofit targets, traditional house-by-house approaches must scale. Neighbourhood retrofit also facilitates community participation. This paper aims to quantitatively characterise the heat energy demand of similar homes in a post-retrofit neighbourhood. The method employs the Modelica AixLib library, dedicated to building performance simulation. A modern semi-detached house is modelled as thermal network. The passive thermal network is calibrated against an equivalent EnergyPlus model. The developed Modelica model then generates time series heat energy demand to meet occupant comfort. This model separates heating for internal space and domestic hot water. Simulation results are gathered for a range of house occupancy profiles, with varying heating schedules and occupant quantities. The calibration results compare the time series of internal house temperature produced by the EnergyPlus and Modelica simulations. Modelica simulations of two heating schedules generate distinct annual demand curves against occupant quantity. As expected in a modern house, domestic hot water accounts for a relatively high proportion of heat energy. Over a year it ranges between 20 and 45% depending on occupant profile. Overall conclusions are threefold. Firstly, occupant profiles of a modern semi-detached house increase annual heat energy demand by 77%, and the coincidence of daily peak demand persists across occupant profiles. Furthermore, percentages of domestic hot water demand start from 20 or 24% and plateau at 39 or 45% depending on space heating schedule. A statistical distribution of energy demand by neighbourhood homes is possible. Its curve plot is not perfectly normal, skewing to larger energy demands.
引用
收藏
页码:217 / 229
页数:13
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