Life cycle costing analysis of deep energy retrofits of a mid-rise building to understand the impact of energy conservation measures

Authors: Haonan Zhang

License: CC BY 4.0

Abstract: Building energy retrofits have been identified as key to realizing climate mitigation goals in Canada. This study aims to provide a roadmap for existing mid-rise building retrofits in order to understand the required capital investment, energy savings, energy cost savings, and carbon footprint for mid-rise residential buildings in Canada. This study employed EnergyPlus to examine the energy performance of 11 energy retrofit measures for a typical multi-unit residential building (MURB) in Metro Vancouver, British Columbia, Canada. The author employed the energy simulation software (EnergyPlus) to evaluate the pre-and post-retrofit operational energy performance of the selected MURB. Two base building models powered by natural gas (NG-building) and electricity (E-building) were created by SketchUP. The energy simulation results were combined with cost and emission impact data to evaluate the economic and environmental performance of the selected energy retrofit measures. The results indicated that the NG-building can produce significant GHG emission reductions (from 27.64 tCO2e to 3.77 tCO2e) by implementing these energy retrofit measures. In terms of energy savings, solar PV, ASHP, water heater HP, and HRV enhancement have great energy saving potential compared to other energy retrofit measures. In addition, temperature setback, lighting, and airtightness enhancement present the best economic performance from a life cycle perspective. However, windows, ASHP, and solar PV, are not economical choices because of higher life cycle costs. While ASHP can increase life cycle costs for the NG-building, with the financial incentives provided by the governments, ASHP could be the best choice to reduce GHG emissions when stakeholders make decisions on implementing energy retrofits.

Submitted to arXiv on 02 Apr. 2023

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.