Since 2020, non-linear multiple regressions have been acknowledged as a viable method for estimating optimal heat recovery. With the usage of four additional influence parameters, a multiple regression would require an exponential number of around 8,000 data sets, whereas the partial and relational model requires only seven individual, singular regressions in combination (the original three plus additional four factors). The non-linear multiple regressions were already discussed and partially considered as a parallel track in the revision process of the Eco Design Regulation EU 1253/2014. Due to the rapid fluctuation of energy prices as well as the need to accommodate other influencing factors, this study proposes an alternative modeling technique that utilizes a combination of non-linear singular and individual regressions. Change factors were used to represent the optimal temperature transfer efficiency and the required air velocity. Furthermore, this study suggests calculating the pressure drop of the heat recovery without employing regressions but instead utilizing a known average pressure drop and calculating the optimal pressure drop based on alterations in the causal relationships. A partial error analysis was used to evaluate the quality of the individual regressions based on the quality of the individual regressions, comparing the new partial and relational regression models with the non-linear multiple regression.