Accurately measuring the actual building performance would be a significant step toward performance contracting. Methods under occupancy have operational advantages but come with additional constraints due to the variability of weather and occupancy conditions, which cannot be overlooked. Especially in recent, well-insulated buildings, since solar gains are a larger contribution to the global heat balance. This work aims to enhance the accuracy of the Heat Transfer Coefficient estimation in an occupied building by improving solar gains estimation in multilinear regression modelsts. The approach adopted here considers solar gains from both the incident solar heat flux on glazing and from the opaque elements of a building by calculating an equivalent outdoor temperature. This model feeds a multilinear regression model whose parameters are estimated by Bayesian inference. Our model for estimating HTC is validated against a set of virtual data from a simulated detached house under different weather conditions.