Applied Sciences (Sep 2022)

Investigations of Building-Related LCC Sensitivity of a Cost-Effective Renovation Package by One-at-a-Time and Monte Carlo Parameter Variation Methods

  • Yovko Ivanov Antonov,
  • Kim Trangbæk Jønsson,
  • Per Heiselberg,
  • Michal Zbigniew Pomianowski

DOI
https://doi.org/10.3390/app12199817
Journal volume & issue
Vol. 12, no. 19
p. 9817

Abstract

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Nearly Zero Energy Building (NZEB) is becoming a standard for new and renovated buildings throughout the European Union (EU). Through the ongoing implementation of directives related to energy efficiency and NZEB-compliant buildings, the EU commission has established that new and renovated NZEB-compliant buildings shall be implemented cost-effectively. This is assessed by linking the Life Cycle Cost (LCC) and energy demand calculations, representing them in a cost-optimality plot, and finding the optimal solution from the resulting Pareto front. Given that the results of an LCC calculation are quite dependent on the calculation model’s scope and inputs, this study takes an explorative approach to determine the most influential parameters in LCC calculations for a pre-selected cost-effective package. This is achieved by varying the inputs using local and global variation methods. The local variation approach consists of varying the inputs one-at-a-time (OAT), whereas with global variation, all the selected inputs are variated simultaneously. The OAT approach identified the amount and unit cost of the utility supply (district heating, electricity, and gas) as the most influential parameters to the output. The OAT results were further used to rank the next five most sensitive parameters and perform a global sensitivity analysis using Monte Carlo (MC) simulations. A regression analysis of the MC results revealed high R2 values (≥0.98), suggesting a linear correlation between the output and the variable inputs. The sensitivity analysis determined the unit price of attic insulation, the gas price, and the lifetime of the Heat Pump (HP) as the most sensitive parameters in the three investigated models.

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