Probabilistic life cycle costing of existing buildings retrofit interventions towards nZE target: Methodology and application example

One of the major challenge facing the achievement of nZE standards in existing buildings is the economic issue: the evidence of monetary gains of energy savings facing high investment costs seems still rather limited to the investors’ eyes. In this context, LCC methods have gained much importance in recent years. However, they present a limitation due to the notable simplifications and hypothesis usually made for input parameters that may affect the results. In order to overcome this limit, this work suggests a probabilistic LCC based on uncertainty and sensitivity analysis via Monte Carlo methods and illustrates it through a building case study under several retrofit scenarios sighting the target nZE. The methodology allows investigating the economic effectiveness of alternative measures, giving insight into possible ranges of the economic indicator related to a specific design option. The analysis is focused on a micro-economic dimension and based on the availability and reliability of inputs data and on their proper characterization with Probability Density Functions. Variance-based methods for sensitivity analysis are employed to establish the most influential parameters on output uncertainty. The paper demonstrates the potentials of a probabilistic LCC in providing a more realistic decision support about investments for energy efficient projects.