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

  • Paul BeagonEmail author
  • Fiona Boland
  • James O’Donnell
Conference paper
Part of the Springer Proceedings in Energy book series (SPE)

Abstract

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.

Keywords

Building retrofit Building simulation Modelica AixLib library Neighbourhood scale Statistical distribution 

Notes

Acknowledgements

Paul Beagon gratefully acknowledges that his research is supported in part by a research grant from Science Foundation Ireland (SFI) under the SFI Strategic Partnership Programme Grant Number SFI/15/SPP/E3125.

Paul thanks Moritz Lauster of RWTH Aachen University and E. ON Energy Research Center, for his kind and useful advice on AixLib library.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paul Beagon
    • 1
    • 2
    Email author
  • Fiona Boland
    • 3
  • James O’Donnell
    • 1
    • 2
  1. 1.School of Mechanical and Materials EngineeringUniversity College DublinDublinIreland
  2. 2.Energy Institute, University College DublinDublinIreland
  3. 3.Royal College of Surgeons in IrelandDublinIreland

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