The use of regression analysis for the identification of building performance parameters based on measurements is often difficult due to collinearity between the outdoor temperature and the global solar radiation (S). This study proposes a method to overcome this issue. The proposed method is based on using the seasonal symmetry of S to pair data from time-periods equidistant from the winter solstice. In addition, a method to utilize synthetic data to fine-tune the paired-data approach is presented. To evaluate the paired-data approach, two years data from a multifamily building in Umea was used to estimate the heat loss factor (air-to-air transmission including air leakage). The results were compared with results obtained when S was very low (S 0). It was found that, the fine-tuned paired-data approach resulted in a modest deviation in the heat loss factor with an average absolute deviation of 4.0%. The small deviation indicates that the paired-data approach can extend the use of single-variate regression models for accurate identification of heat loss factors to situations where the solar gain is substantial. The paired-data approach was also used to calibrate a commercial energy building simulation tool. (C) 2016 Elsevier B.V. All rights reserved.