## 1 Introduction  
Within the context of European regulation on energy efficiency and energy conservation, the passive house (PH) concept has emerged as a global quality assurance standard [1; 2; 3]. Nearly Zero Energy Buildings (NZEBs) have become an essential element in developed countries to achieve a reduction in energy consumption and CO\({}_{2}\) in the construction sector [4; 5; 6], using efficient systems of HVAC (Heating, Ventilating, and Air-Conditioning) and increasing the thermal insulation of buildings [7]. Indeed, PH requires 80-90% less heating energy than conventional buildings to provide optimal thermal comfort conditions, while the initial investment only represents an increase of 5-10% [8]. However, the main barriers for this type of construction are the high performance building materials and the cost of adoption (training and certification) [9; 10]. Some authors stated that a PH should be defined by six principles: (i) a high level of thermal insulation and thermal capacity of opaque walls [3; 5; 6; 7; 11], (ii) minimization of thermal bridges [3; 5; 11; 12], (iii) high efficient windows [5; 12], (iv) high levels of airtightness [3; 5; 6; 7], (v) passive solar gains [7; 11; 12], and (vi) efficient mechanical ventilation with heat recovery system [3; 5; 6; 7]. For this reason, researchers put their efforts on the assessment of the energy performance of PH dwellings. The main approaches consisted of post-occupancy surveys, measurements of the actual energy consumption, life-cycle analyses (LCA) in dynamic conditions, using simulation (i.e., EcoHestia, Computation Fluid Dynamics (CFD) model, or EnergyPlus) [6; 11], and the determination of the thermal comfort by the PMV (Predicted Mean Vote) model according to ASHRAE 55 and EN 15,251 [4; 5; 7; 8; 9; 11; 13]. As regards the design parameters, recent studies were focused on building performance optimization (BPO) through the implementation of meta-models (i.e., multiple linear regression, support vector machines (SVM), and artificial neural networks (ANN)) [8]. Nevertheless, a lack of systematic optimization methods for facades is still detected in the research field [8]. Besides this, few studies assessed the in situ performance of the building fabric of PH dwellings in comparison with the theoretical design [3; 14; 15; 16; 17; 18]. Along this line, [PERSON] et al. [3] highlighted that most of in situ tests were conducted on prefabricated timber frames or externally insulated thin-joint blockworks, using heat flux meter (_HFM_) and qualitative infrared thermography (_IRT_) techniques. No references were found for heavy multi-leaf walls and quantitative internal IRT. The heat flux meter (_HFM_) method is widely used as a non-destructive method for measuring the actual thermal transmittance of facades [19; 20; 21; 22; 23; 24; 25]. However, the literature review revealed that the _HFM_ average method is not frequently applied to verify compliance with technical specifications of projects in facades with low U-value in PH on site. It is challenging to obtain accurate results during in situ measuring of facades with low thermal transmittance. Very few initiatives have conducted in situ measurements on facades with low U-values, using the _HFM_ average method. Furthermore, in most of these studies, the relative deviation of measured U-values from theoretical values was significant. [PERSON] et al. [21] measured the actual thermal transmittance of facades, with theoretical U-values ranging from 0.23 up to 0.33 W/m\({}^{2}\)-K. Their results showed relative deviations of between 4 to 75%. [PERSON] et al. [26] obtained a relative deviation between theoretical and measured U-values of 28% when they monitored, in situ, a building envelope with theoretical thermal transmittance of 0.20 W/m\({}^{2}\)-K. [PERSON] et al. [27] and [PERSON] et al. [28] used the _HFM_ average method to validate results obtained by using the quantitative infrared thermography technique. Researchers obtained relative deviations between U-values from 1 up to 6% and of 83%, respectively.