This thesis preliminarily explores the use of fuzzy numbers in two applications: life-cycle assessment (LCA) and building energy analysis. In LCA, this thesis compares the results in the context of concept selection between the fuzzy number and Monte Carlo approaches and finds that the numerical outcomes of both approaches are comparable.
In building energy analysis, the traditional degree-day method is adapted by allowing the data inputs as fuzzy numbers. Instead of providing the “average point” results, the fuzzy number approach can yield an “interval” of energy estimations. The proposed fuzzy number approach has been applied to a building located in Calgary, and the results are compared to historical data and building energy simulation (i.e., eQUEST).
As the ranking of fuzzy numbers is fundamental to support decision making in both applications, this thesis also investigates the axiomatic properties of one well-known ranking method, namely, the centroid index method. This thesis has explored using a numerical approach to identify counter-examples in the proof process.