Probabilistic Life Cycle Cost analysis of building energy efficiency measures: selection and characterization of the stochastic inputs through a case study

In recent years, the boost to the realization of “nearly Zero Energy Buildings” (nZEB) could require so high investment costs which may be not justifiable with the reduced consumptions (and costs) during the use phase. So, even if the estimated potential saving of energy efficiency projects seems to be very high, investors are often discouraged by a high-risk perception, linked to difficulty in knowing the real costs of advanced and innovative technologies, assessing unforeseen costs, or taking into account the significant fluctuations in energy costs. Life Cycle Cost Analysis (LCCA) in buildings could be a useful estimation method, but a large set of input parameters and accurate predictions is required to achieve an effective assessment. Aim of this study is the selection and characterization of the stochastic inputs to be exploited in a probabilistic LCCA to find the cost-optimal energy efficiency measures. We developed a Monte-Carlo based methodology for uncertainty quantification and sensitivity analysis, which combines Global Costs calculation with Building Energy Simulation and we applied it to a building case study, representative of the typical Italian stock. We characterized the stochastic inputs typically involved in the Global Cost method (related to the initial Investment Costs, Annual Costs, Residual Values, and Discount Rates) and analyzed the impact of these parameters on the final results in different renovation scenarios. Results showed that the financial factors (inflation and discount rate) and the energy trend uncertainty are the most influential parameters. Nevertheless, pushing towards nZEB makes it increasingly important the accuracy of investment costs data.