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Deep energy renovation of building stock came more into focus in the EuropeanUnion due to energy efficiency related directives. Many buildings that mustundergo deep energy renovation are old and may lack design/renovationdocumentation, or possible degradation of materials might have occurred inbuilding elements over time. Thermal transmittance (i.e. U-value) is one of themost important parameters for determining the transmission heat losses throughbuilding envelope elements. It depends on the thickness and thermal propertiesof all the materials that form a building element. In-situ U-value can bedetermined by ISO 9869-1 standard (Heat Flux Method - HFM). Still, measurementduration is one of the reasons why HFM is not widely used in field testingbefore the renovation design process commences. This paper analyzes thepossibility of reducing the measurement time by conducting parallelmeasurements with one heat-flux sensor. This parallelization could be achievedby applying a specific class of the Artificial Neural Network (ANN) on HFMresults to predict unknown heat flux based on collected interior and exteriorair temperatures. After the satisfying prediction is achieved, HFM sensor canbe relocated to another measuring location. Paper shows a comparison of fourANN 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-termmemory with 100 units, gated recurrent unit with 100 units and combination of50 long short-term memory units and 50 gated recurrent units. The analysis gavepromising results in term of predicting the heat flux rate based on the twoinput temperatures. Additional analysis on another wall showed possiblelimitations of the method that serves as a direction for further research onthis topic.

Thermal transmittance prediction based on the application of artificial neural networks on heat flux method results