This study employs statistical inverse uncertainty analysis to identify the indeterministic building design parameters for cooling energy demand based on comprehensive measurement data. The research procedure involves measuring deterministic design parameters such as lighting, equipment, hot water boiler, and air-conditioning system using energy meters. A building energy model representing the studied house is validated and calibrated against the measured data. Ranges of indeterministic design parameters, namely: the building’s envelope performance, air conditioners’ coefficient of performance, setpoints, and infiltration rate (ACH) are defined. Then, the statistically significant indeterministic design parameters are identified using regression analysis. The analysis indicated that the coefficient of performance is the most substantial parameter, followed by the air infiltration and the building’s envelope performance (thermal bridges of wall, roof, and fenestration). Occupants’ behavior (setpoints) came as the last indeterministic parameter. The inverse uncertainty analysis showed an actual COP from 2 to 2.5, ACH between 1.5 and 2, setpoint between 22 and 23 ℃, and wall and roof insulation thickness of 5 cm.