Ongoing climate action programs are prompting more stringent energy performance standards for buildings. Consequently, energy retrofitting of existing buildings has become a common climate action initiative around the World. Building owners are keen to assess the technical and financial feasibility of energy retrofitting in pre -project planning stages. Life cycle cost analysis (LCCA) involves assessment of the economic cost of an asset or a service. LCCA, which is a data -intensive process, is heavily affected by data uncertainties; hoWever, traditional deterministic LCCA models are oblivious to data uncertainty and this can have a significant effect on the final decision. As a solution, this paper proposes a novel LCCA approach for building energy retrofits. The proposed approach calculates life cycle cost (LCC) by adopting fuzzy set theory. In order to illustrate the proposed methodology, a case study was conducted for a public building operated in the Okanagan region of British Columbia, Canada. Two energy retrofit alternatives a major retrofit and a deep retrofit were compared using the proposed fuzzy set theory -based approach and the traditional deterministic LCCA approach. Even though the deterministic result had a strong membership to the fuzzy membership function of the LCC, the range of possible LCC is wide. Use of the fuzzy-based approach would enable the forecasting of possible future LCC as a result of changes in macro-economic factors. This information would allow building managers to plan for adverse future scenarios. (C)2017 Elsevier B.V. All rights reserved.