  Deep energy renovation of building stock came more into focus in the European
Union due to energy efficiency related directives. Many buildings that must
undergo deep energy renovation are old and may lack design/renovation
documentation, or possible degradation of materials might have occurred in
building elements over time. Thermal transmittance (i.e. U-value) is one of the
most important parameters for determining the transmission heat losses through
building envelope elements. It depends on the thickness and thermal properties
of all the materials that form a building element. In-situ U-value can be
determined by ISO 9869-1 standard (Heat Flux Method - HFM). Still, measurement
duration is one of the reasons why HFM is not widely used in field testing
before the renovation design process commences. This paper analyzes the
possibility of reducing the measurement time by conducting parallel
measurements with one heat-flux sensor. This parallelization could be achieved
by applying a specific class of the Artificial Neural Network (ANN) on HFM
results to predict unknown heat flux based on collected interior and exterior
air temperatures. After the satisfying prediction is achieved, HFM sensor can
be relocated to another measuring location. Paper shows a comparison of four
ANN cases applied to HFM results for a measurement held on one multi-layer wall
- multilayer perceptron with three neurons in one hidden layer, long short-term
memory with 100 units, gated recurrent unit with 100 units and combination of
50 long short-term memory units and 50 gated recurrent units. The analysis gave
promising results in term of predicting the heat flux rate based on the two
input temperatures. Additional analysis on another wall showed possible
limitations of the method that serves as a direction for further research on
this topic.
