Impacts of Uncertainties in Life Cycle Cost Analysis of Buildings Energy Efficiency Measures: Application to a Case Study

Life Cycle Cost (LCC) analysis in the field of building renovation is considered an important decision support of the design process in order to compare the effectiveness of different energy efficiency measures (EEMs). Nevertheless, data uncertainty is a well-recognised issue associated with LCC deterministic calculation methods and probabilistic methodologies could instead provide a more effective decision support. This paper proposes a Monte Carlo based methodology for uncertainty quantification that combines parametric building simulation and LCC analysis, showing a great potential in the possibility of combining several EEMs and undertake the uncertainty calculation with low computational costs and high accuracy of the result. The work aimed to identifying and quantifying the main uncertain inputs of the LCC assessment and developing a tools suite to automate the process of evaluation of the energy impact due to the combination of several EEMs and quantification of the uncertainty distribution of the output. Results from the application to a case study are mainly intended to illustrate the methodology application and underline the impact that input uncertainties may have on the output variable. The difficulty to identify the robust EEMs is particularly due to the great influence of macroeconomic parameters uncertainty used in the calculation.