The dynamic integration of building retrofit investment tools and linear power systems optimisation tools requires the development of simplified linear building energy models which are representative of different Energy Conservation Measures (ECMs) options. Ensemble Calibration is a methodology which identifies linear building energy models as functions of ECMs for opaque building envelope components. The methodology uses Particle Swarm Optimisation (PSO), a heuristic optimisation algorithm, to minimise the calibration error between model predictions and suitable baseline data. The Ensemble Calibration methodology cannot model fast building thermal response characteristics, such as glazing parameters (e.g., thermal transmittance and solar transmittance) or air leakage parameters (e.g., infiltration rate), as functions of transparent envelope ECMs. The standard PSO algorithm widely explores the solution space while attracting all particles (i.e., candidate model solutions) to the best solution at each iteration. Fast building response parameters are significantly altered during the early iterations of the PSO algorithm, thus having a negative impact on the overal calibration process. Therefore, the glazing and infiltration parameters are not correctly identified in an Ensemble Calibration framework and calibration accuracy of the building models suffers as a result. The current paper addresses this issue through the augmentation of existing Ensemble models using supplementary retrofit parameter functions for non-opaque ECMs. The paper also proposes a simplified infiltration model which emulates improvements in air tightness associated with the addition of ECMs while enabling other air tightness measures to be included as ECMs. The proposed methodology is applied to the Ensemble Calibration of two EnergyPlus archetype models representative of the detached housing stock and mid-floor apartment stock in Ireland. The augmentation algorithm results in the accurate calibration of linear building energy models for different ECM configurations (i.e., ECM combination options), while providing considerable computational advantages. The proposed methodology enables the use of glazing and infiltration scenarios in an Ensemble Calibration framework, thus enhancing the representativeness of the methodology for the integrated analysis of ECM investment planning under future electrified space heating scenarios.