Now showing 1 - 2 of 2
  • Publication
    Quantitative evaluation of deep retrofitted social housing using metered gas data
    Research into home energy retrofit is important because most existing homes will operate in 2050. A lack of funding or incentives often prevents home energy retrofit, particularly of social housing. This study analysed retrofitted Irish social housing and their gas meter data, including pre-payment meters that require regular “top-ups” purchased from shops. The data comprised records from 100 retrofit and control group homes throughout 2013–2015. A novel evaluation of retrofitted rented homes processed meter data into multiple metrics. Gas consumption is computed per house and weather correction is incorporated, enabling statistical testing of the retrofit. A “difference in difference” technique compared the retrofit and control groups. Gas consumptions of the most popular building type are plotted as distribution curves before and after retrofit. Subsequently the energy use intensity (kWh/m2/year) is computed per home; leading to calculation of the prebound effect. In social housing, the prebound effect quantifies energy underconsumption due to self-rationing. Retrofit significantly reduced gas consumption, and reduced its variance among homes. A small positive skewness in the statistical distribution of home gas consumption prevented characterisation as a normal distribution. The prebound effect is high, but alleviated by the retrofit. Finally, retrofit extended average pre-payment intervals.
      575Scopus© Citations 9
  • Publication
    Operational characterisation of neighbourhood heat energy after large-scale building retrofit
    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 semidetached 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.